From lila.kari at uwo.ca Sat Mar 1 19:58:30 2014 From: lila.kari at uwo.ca (Lila Kari) Date: Sat, 01 Mar 2014 19:58:30 -0500 Subject: Connectionists: UCNC 2014 - Last CFP Message-ID: UCNC 2014 - LAST CALL FOR PAPERS The 13th International Conference on Unconventional Computation & Natural Computation University of Western Ontario, London, Ontario, Canada July 14-18, 2014 http://www.csd.uwo.ca/ucnc2014 http://www.facebook.com/UCNC2014 https://twitter.com/UCNC2014 Submission deadline: March 7, 2014 OVERVIEW The International Conference on Unconventional Computation and Natural Computation has been a meeting where scientists with different backgrounds, yet sharing a common interest in novel forms of computation, human-designed computation inspired by nature, and the computational aspects of processes taking place in nature, present their latest results. Papers and poster presentations are sought in all areas, theoretical or experimental, that relate to unconventional computation and natural computation. Typical, but not exclusive, topics are: * Molecular (DNA) computing, Quantum computing, Optical computing, Hypercomputation - relativistic computation, Chaos computing, Physarum computing, Computation in hyperbolic spaces, Collision-based computing, Computations beyond the Turing model; * Cellular automata, Neural computation, Evolutionary computation, Swarm intelligence, Ant algorithms, Artificial immune systems, Artificial life, Membrane computing, Amorphous computing; * Computational Systems Biology, Genetic networks, Protein-protein networks, Transport networks, Synthetic biology, Cellular (in vivo) computing. INVITED PLENARY SPEAKERS Yaakov Benenson (ETH Zurich) - "Molecular computing meets synthetic biology" Charles Bennett (IBM Research) - "Thermodynamics of computation and self-organization" Hod Lipson (Cornell University) - "The robotic scientist" Nadrian Seeman (New York University) - "DNA: not merely the secret of life" INVITED TUTORIAL SPEAKERS Anne Condon (University of British Columbia) - "Programming with biomolecules" Ming Li (University of Waterloo) - "Approximating semantics" Tommaso Toffoli (Boston University) - "Do we compute to live, or live to compute?" WORKSHOPS Computational Neuroscience - Organizer: Mark Daley (University of Western Ontario) DNA Computing by Self-Assembly - Organizer: Matthew Patitz (University of Arkansas) Unconventional Computation in Europe - Organizers: Martyn Amos (Manchester Metropolitan University), Susan Stepney (University of York) IMPORTANT DATES Submission deadline: March 7, 2014 Notification of acceptance: April 7, 2014 Final versions due: April 27, 2014 Conference: July 14-18, 2014 INSTRUCTIONS FOR AUTHORS Authors are invited to submit original papers (at most 12 pages in LNCS format) or one-page poster abstracts using the link https://www.easychair.org/conferences/?conf=ucnc2014 Papers must be submitted in Portable Document Format (PDF). The revised version of the manuscripts, to appear in a LNCS volume by Springer available at the conference venue, must be prepared in LATEX, see http://www.springer.com/computer/lncs/lncs+authors The papers must not have been submitted simultaneously to other conferences with published proceedings. All accepted papers must be presented at the conference. Selected papers will appear in a special issue of Natural Computing. PROGRAM COMMITTEE Andrew Adamatzky (University of the West of England, UK) Selim G. Akl (Queen's University, Canada) Eshel Ben-Jacob (Tel-Aviv University, Israel) Cristian S. Calude (University of Auckland, New Zealand) Jose Felix Costa (IST University of Lisbon, Portugal) Erzsebet Csuhaj-Varju (Eotvos Lorand University, Hungary) Alberto Dennunzio (Universita degli Studi di Milano-Bicocca, Italy) Marco Dorigo (Universite Libre de Bruxelles, Belgium) Jerome Durand-Lose (Universite d'Orleans, France) Masami Hagiya (University of Tokyo, Japan) Oscar H. Ibarra (University of California, Santa Barbara, USA, Co-Chair) Kazuo Iwama (Kyoto University, Japan) Jarkko Kari (University of Turku, Finland) Lila Kari (University of Western Ontario, Canada, Co-Chair) Viv Kendon (University of Leeds, UK) Kamala Krithivasan (IIT Madras, India) Giancarlo Mauri (Universita degli Studi di Milano-Bicocca, Italy) Yongli Mi (Hong Kong University of Science and Technology, China) Mario J. Perez-Jimenez (Universidad de Sevilla, Spain) Kai Salomaa (Queen's University, Canada) Hava Siegelmann (University of Massachusetts Amherst, USA) Susan Stepney (University of York, UK) Damien Woods (California Institute of Technology, USA) Byoung-Tak Zhang (Seoul National University, Korea) From g.goodhill at uq.edu.au Sun Mar 2 04:28:58 2014 From: g.goodhill at uq.edu.au (Geoffrey Goodhill) Date: Sun, 2 Mar 2014 19:28:58 +1000 Subject: Connectionists: PhD opportunities at the Queensland Brain Institute Message-ID: The Queensland Brain Institute (QBI - http://www.qbi.uq.edu.au) is seeking exceptional and highly motivated PhD students to undertake neuroscience research at its state-of-the-art facility located on The University of Queensland?s (UQ) St Lucia campus, Brisbane, Queensland, Australia. Established in 2003, the QBI's multidisciplinary environment is home to more than 300 staff and approximately 100 PhD students. Key areas of interest include computation and neuronal circuits, cognition and behaviour, neurogenesis and neuronal survival, genetics and epigenetics, neuronal development and connectivity, sensory systems, and synaptic function. QBI researchers have published almost 500 papers in the past two years, including 2 in Science, 4 in Nature, 2 in Neuron, 3 in Nature Neuroscience, 8 in Nature Genetics, 8 in Proceedings of the National Academy of Sciences of the USA, and 18 in Journal of Neuroscience. Scholarships and Remuneration QBI offers full-time PhD scholarships for outstanding Australian and International students (living allowance AUD$25,392 per annum, indexed annually, tax exempt) for 3 years with a possible 6 month extension. In addition, QBI supports high quality domestic and international applicants in their nomination to The University of Queensland?s competitive scholarship rounds (three rounds per year). A QBI top-up scholarship of $5,000 per annum may also be available to successful candidates. The Candidate Both Australian and International applicants are welcome to apply. Candidates should have a First Class Honours degree (or equivalent) in neuroscience, molecular biology, bioscience, psychology, genetics, the physical sciences, engineering, mathematics, computer science, or a related discipline. Strong academic performance, the commitment to conduct high quality neuroscience research, and published output are highly desirable. For further information on the Basis of Admission to a UQ research higher degree, please visit http://www.uq.edu.au/grad-school/our-research-degrees. Successful applicants must accept and commence within 6 months of receiving an award. Enquiries and Applications Please review the research interests of QBI Group Leaders at http://www.qbi.uq.edu.au/group-leaders and make direct contact with a Group Leader working in an area of greatest interest to you, providing your detailed academic resume and complete academic records (including GPA scores/grades, and grading scale details). The Group Leader will work with you to develop your application for submission to QBI. APPLICATIONS CLOSE ON 30 APRIL, 2014 Professor Geoffrey J Goodhill Queensland Brain Institute and School of Mathematics & Physics University of Queensland St Lucia, QLD 4072, Australia Phone: +61 7 3346 6431 Fax: +61 7 3346 6301 Email: g.goodhill at uq.edu.au http://www.qbi.uq.edu.au/professor-geoffrey-goodhill -------------- next part -------------- An HTML attachment was scrubbed... URL: From dwang at cse.ohio-state.edu Sun Mar 2 15:34:45 2014 From: dwang at cse.ohio-state.edu (DeLiang Wang) Date: Sun, 2 Mar 2014 15:34:45 -0500 Subject: Connectionists: NEURAL NETWORKS, March 2014 Message-ID: <531395E5.8020102@cse.ohio-state.edu> Neural Networks - Volume 51, March 2014 http://www.journals.elsevier.com/neural-networks Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networks Jiejie Chen, Zhigang Zeng, Ping Jiang Feature selection and multi-kernel learning for sparse representation on a manifold Jim Jing-Yan Wang, Halima Bensmail, Xin Gao Long-term time series prediction using OP-ELM Alexander Grigorievskiy, Yoan Miche, Anne-Mari Ventela, Eric Severin, Amaury Lendasse Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler Guoqiang Li, Peifeng Niu, Huaibao Wang, Yongchao Liu Neural network for solving convex quadratic bilevel programming problems Xing He, Chuandong Li, Tingwen Huang, Chaojie Li Stability analysis of switched stochastic neural networks with time-varying delays Xiaotai Wu, Yang Tang, Wenbing Zhang Lagrangian support vector regression via unconstrained convex minimization S. Balasundaram, Deepak Gupta, Kapil Periodicity and global exponential stability of generalized Cohen-Grossberg neural networks with discontinuous activations and mixed delays Dongshu Wang, Lihong Huang A generalized analog implementation of piecewise linear neuron models using CCII building blocks Hamid Soleimani, Arash Ahmadi, Mohammad Bavandpour, Ozra Sharifipoor From marcel.van.gerven at gmail.com Sun Mar 2 16:21:26 2014 From: marcel.van.gerven at gmail.com (Marcel van Gerven) Date: Sun, 2 Mar 2014 22:21:26 +0100 Subject: Connectionists: PRNI Final call for papers In-Reply-To: References: Message-ID: FINAL CALL FOR PAPERS ** apologies for cross posting ** ** paper submission deadline extended to March 17 ** 4th International Workshop on Pattern Recognition in NeuroImaging (PRNI 2014) June 4-6 2014, Max Planck Institute for Intelligent Systems, T?bingen, Germany Website: http://www.prni.org Multivariate analysis of neuroimaging data has gained ground very rapidly in the community over the past few years, leading to impressive results in cognitive and clinical neuroscience. Pattern recognition and machine learning conferences regularly feature a neuroimaging workshop, while neuroscientific meetings dedicate sessions to new approaches to neural data analysis. Thus, a rich two-way flow has been established between disciplines. It is the goal of the 4th International Workshop on Pattern Recognition in NeuroImaging to continue facilitating exchange of ideas between scientific communities, with a particular interest in new approaches to the interpretation of neural data driven by new developments in pattern recognition and machine learning. IMPORTANT DATES Paper submission deadline: 17th of March, 2014 **submission website is now open** Acceptance notification: 11th of April, 2014 Camera ready paper submission: 18th of April 2014 Workshop: June 4-6, 2014 TOPICS OF INTEREST PRNI welcomes original papers on multivariate analysis of neuroimaging data, using invasive and non-invasive imaging modalities, including but not limited to the following topics: * Learning from neuroimaging data - Classification algorithms for brain-state decoding - Optimisation and regularisation - Bayesian analysis of neuroimaging data - Connectivity and causal inference - Combination of different data modalities - Efficient algorithms for large-scale data analysis * Interpretability of models and results - High-dimensional data visualisation - Multivariate and multiple hypothesis testing - Summarisation/presentation of inference results * Applications - Disease diagnosis and prognosis - Real-time decoding of brain states - Analysis of resting-state and task-based data KEYNOTE SPEAKERS John-Dylan Haynes Klaus-Robert M?ller Russ Poldrack SUBMISSION GUIDELINES Authors should prepare full papers with a maximum length of 4 pages (double-column, IEEE style, PDF) for review. Reviews will be double-blind, i.e. submissions have to be anonymized. PROCEEDINGS Proceedings will be published by Conference Publishing Services in electronic format. They will be submitted for inclusion in IEEExplore and IEEE CS Digital Library online repositories, and submitted for indexing in IET INSPEC, EI Compendex (Elsevier), Thomson ISI, and others. BEST STUDENT PAPER AWARD A small number of papers will be selected for the Best Student Paper Award. To be eligible the paper?s first author must be a student, and the student must agree to present the paper at the workshop. Awardees will receive a travel allowance. VENUE The workshop will be held on the campus of the Max Planck Institute in T?bingen, Germany. T?bingen is a picturesque medieval university town, and can easily be reached by public transportation from Stuttgart airport (STR). Accommodation is available on or close to campus. A pre-conference barbecue will be held on June 3, 2014. ORGANIZATION General Chair: Moritz Grosse-Wentrup (MPI for Intelligent Systems, T?bingen, Germany) Program Chairs: Marcel van Gerven (Donders Institute for Brain, Cognition and Behaviour, Netherlands) & Nikolaos Koutsouleris (LMU Munich, Germany) Steering committee: Jonas Richiardi, Dimitri Van De Ville, Seong-Whan Lee, Yuki Kamitani, Janaina Mourao- Miranda, Christos Davatsikos, Ga?l Varoquaux ENDORSEMENTS PRNI 2014 is an official satellite meeting of the Organization for Human Brain Mapping and an endorsed event of the Medical Image Computing and Computer Assisted Intervention Society. -------------- next part -------------- An HTML attachment was scrubbed... URL: From peter.ljunglof at heatherleaf.se Mon Mar 3 05:33:49 2014 From: peter.ljunglof at heatherleaf.se (=?iso-8859-1?Q?peter_ljungl=F6f?=) Date: Mon, 3 Mar 2014 11:33:49 +0100 Subject: Connectionists: EACL 2014: Call for Participation Message-ID: <1978094D-E288-4005-B6BC-034C618ECAFC@heatherleaf.se> CALL FOR PARTICIPATION EACL 2014 The 14th Conference of the European Chapter of the Association for Computational Linguistics Gothenburg, Sweden 26-30 April 2014 http://eacl2014.org/ The 14th Conference of the European Chapter of the Association for Computational Linguistics covers research in all aspects of automated natural language processing, including but not limited to the following areas: - computational and cognitive models of language acquisition and language processing - information retrieval and question answering - generation and summarization - language resources and evaluation - machine learning methods and algorithms for natural language processing - machine translation and multilingual systems - phonetics, phonology, morphology, word segmentation, tagging, and chunking - pragmatics, discourse, and dialogue - semantics, textual entailment - social media, sentiment analysis and opinion mining - spoken language processing and language modeling - syntax, parsing, grammar formalisms, and grammar induction - text mining and natural language processing applications Apart from regular paper sessions and poster sessions, EACL 2014 will contain workshops, tutorials, invited speakers and several social events. REGISTRATION EACL 2014 registration is open! All information ca be found at the following web page: http://eacl2014.org/registration Early registration deadline is 3 March 2014. Late registration deadline is 24 April 2014. Online registration will not be available after 24 April, you can however register on-site at the conference. EACL WORKSHOPS The following workshops will be held during 26-27 April. - CAtoCL: Workshop on Computational Approaches to Causality in Language - CLFL: 3rd Workshop on Computational Linguistics for Literature - CVSC: 2nd Workshop on Continuous Vector Space Models and their Compositionality - CogACLL: Workshop on Cognitive Aspects of Computational Language Learning - DM: Dialogue in Motion - HaCaT: Workshop on Humans and Computer-assisted Translation - HyTra: 3rd Workshop on Hybrid Approaches to Machine Translation - LASM: 5th Workshop on Language Analysis for Social Media - LaTeCH: 8th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities - Louhi: 5th International Workshop on Health Text Mining and Information Analysis - MWE: 10th Workshop on Multiword Expressions - PITR: 3rd Workshop on Predicting and Improving Text Readability for Target Reader Populations - TTNLS: Type Theory and Natural Language Semantics - WaC-9: 9th Web as Corpus workshop For details, see: http://eacl2014.org/workshops EACL TUTORIALS The following workshops will be given during 26-27 April. - Computational modelling of metaphor, by Ekaterina Shutova and Tony Veale - Natural language processing for social media, by Kalina Bontcheva and Leon Derczynski - Recent advances in dependency parsing, by Ryan McDonald and Joakim Nivre - Describing images in natural language, by Julia Hockenmaier - Structured sparsity in natural language processing: Models, algorithms and applications, by Andr? F. T. Martins, M?rio A. T. Figueiredo, Noah A. Smith, and Dani Yogatama - Building models to reveal how natural speech is represented in the brain, by Alexander Huth For details, see: http://eacl2014.org/tutorials INVITED SPEAKERS During the main conference, 28-30 April, we will have the following three invited speakers. - Simon King, University of Edinburgh, UK - Ulrike von Luxburg, University of Hamburg, Germany - Dan Roth, University of Illinois at Urbana-Champaign For details, see: http://eacl2014.org/invited-speakers MAIN PROGRAM Apart from the above, the main program will contain regular paper sessions, poster sessions, software demonstrations, as well as several social events. For details, see http://eacl2014.org/program A list of all accepted papers can be found here: http://eacl2014.org/accepted-papers -- You received this message because you are subscribed to the Google Groups "SIG-SLPAT" group. To post to this group, send email to sig-slpat at googlegroups.com. Visit this group at http://groups.google.com/group/sig-slpat. From i.bojak at reading.ac.uk Mon Mar 3 12:42:44 2014 From: i.bojak at reading.ac.uk (Ingo Bojak) Date: Mon, 3 Mar 2014 17:42:44 +0000 Subject: Connectionists: 3rd International Conference on Neural Field Theory - From Modelling to Data Assimilation Message-ID: An HTML attachment was scrubbed... URL: From ecai2014 at guarant.cz Tue Mar 4 08:40:02 2014 From: ecai2014 at guarant.cz (=?utf-8?q?ECAI_2014?=) Date: Tue, 04 Mar 2014 14:40:02 +0100 Subject: Connectionists: =?utf-8?q?ECAI_2014_-_Tutorials?= Message-ID: <20140304134002.F2093174703@gds25d.active24.cz> ** apologies for cross-posting ** ECAI 2014 Conference August 18-22, 2014 Prague, Czech Republic TUTORIALS The tutorials will be scheduled on August 18 and 19, 2014. As in former editions, the conference registration will include free access to all tutorials. However, for organization convenience, we kindly ask you to register for all tutorials you are interested in, in advance. 18.8. 9:00-12:30 T1 - Constraint Processing: From Algorithms to Applications - Roman Bartak 18.8. 9:00-12:30 T2 - Temporal Representation and Reasoning in Interval Temporal Logics - Dario Della Monica, Angelo Montanari, Pietro Sala 18.8. 9:00-10:30 T3 - The Yesterday, Today and Tomorrow of Parallelism in Declarative Programming - Enrico Pontelli 18.8. 11:00-12:30 T4 - Metalearning & Algorithm Selection - Pavel Brazdil, Carlos Soares, Joaquin Vanschoren 18.8. 14:00-17:30 T5 - Software Engineering for Search and Optimization Problems - Luca Di Gaspero, Tommaso Urli 18.8. 14:00-17:30 T6 - Formal Methods for Event Processing - Alexander Artikis, Georgios Paliouras 18.8. 14:00-15:30 T7 - Tabling for Planning - Neng-Fa Zhou 18.8. 16:00-17:30 T8 - Probabilistic Programming - Luc De Raedt, Angelika Kimmig 19.8. 9:00-12:30 T9 - Introduction to Statistical and Behavioural Analysis of Agent-Based Models - Tamas Mahr, Laszlo Gulyas, Richard Oliver Legendi 19.8. 9:00-10:30 T10 - Procedural Content Generation in Games - Noor Shaker 19.8. 11:00-12:30 T11 - Replication and Recomputation in Scientific Experiments - Ian Gent, Lars Kotthoff 19.8. 14:00-17:30 T12 - Search Methods for Classical and Temporal Planning - Jussi Rintanen 19.8. 14:00-15:30 T13 - Intelligent Socio Technical Interaction - Virginia and Frank Dignum 19.8. 16:00-17:30 T14 - Multilingual Semantic Processing - Roberto Navigli, Andrea Moro More information is available on http://www.ecai2014.org/tutorials/ Agostino Dovier and Paolo Torroni (ECAI 2014 Tutorials Chairs) From john.murray at aya.yale.edu Tue Mar 4 16:55:52 2014 From: john.murray at aya.yale.edu (John D. Murray) Date: Tue, 4 Mar 2014 14:55:52 -0700 Subject: Connectionists: Computational & Cognitive Neuroscience Summer School, in Shanghai Message-ID: We're pleased to announce the 5th Computational and Cognitive Neuroscience Summer School which will take place in Shanghai, China from the 5th to the 23rd of July. Applications are being accepted now on the Cold Spring Harbor Asia website, www.csh-asia.org/summerschool_applinstruct.htm and course information is on the CCN official website www.ccnss.org. The application deadline is March 15. *** Please note that the expenses of the summer school including tuition fee, housing and meals will be fully waived. *** Designed to emphasize higher cognitive functions and their underlying neural circuit mechanisms, the course aims at training talented and highly motivated students and postdoctoral fellows from Asia and other countries in the world. Applicants with quantitative (including Physics, Mathematics, Engineering and Computer Science) or experimental background are welcomed. The lectures will introduce the basic concepts and methods, as well as cutting-edge research on higher brain functions such as decision-making, attention, learning and memory. Modeling will be taught at multiple levels, ranging from single neuron computation, microcircuits and large-scale systems, to normative theoretical approach. Python and Matlab-based programming labs coordinated with the lectures will provide practical training in important computational methods. The summer school is co-organized by Xiao-Jing Wang (NYU & NYU Shanghai), Si Wu (Beijing Normal University), Zach Mainen (Champalimaud Neuroscience Program) and Upi Bhalla (NCBS) and sponsored by Beijing Normal University, East China Normal University, NYU Shanghai, Hugo Shong and the McGovern Institute, and Cold Spring Harbor Asia. Organizers: - Xiao-Jing Wang (NYU) - Zach Mainen (Champalimaud Neurosc. Program) - Upi Bhalla (Nat. Centre for Biological Sciences) - Si Wu (Beijing Normal Univ.) Lecturers: - Bill Newsome (Stanford Univ.) - Bob Desimone (Massachusetts Inst. of Tech.) - Gustavo Deco (Univ. Pompeu Fabra) - Gonzalo G. de Polavieja (Cajal Inst.) - Kenji Doya (Okinawa Inst. Science Technology) - Mariano Sigman (Univ. Buenos Aires) - Michael Breakspear (Queensland Inst. Medical Research) - Michael Hausser (Univ. College London) - Misha Tsodyks (Weizmann Inst.) - Philip Sabes (Univ. California San Francisco) - Sophie Deneve (Ecole Normale Superieure) Feel free to direct any questions to: john.murray at nyu.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From peter.ljunglof at heatherleaf.se Wed Mar 5 05:34:48 2014 From: peter.ljunglof at heatherleaf.se (=?iso-8859-1?Q?peter_ljungl=F6f?=) Date: Wed, 5 Mar 2014 11:34:48 +0100 Subject: Connectionists: CALL FOR PARTICIPATION: EACL 2014 Tutorial in Natural Language Processing for Social Media Message-ID: <65B535F0-091B-4E4A-9235-4B3C2E051776@heatherleaf.se> CALL FOR PARTICIPATION EACL Tutorial in Natural Language Processing for Social Media Gothenburg, Sweden, 26 April 2014 http://eacl2014.org/tutorial-social-media There is an increasing need to interpret and act upon information from large-volume, social media streams, such as Twitter, Facebook, and forum posts. However, NLP methods face difficulties when processing social media text. We call for participation in an intermediate-to-advanced level tutorial, discussing the state of the art in processing social media text. Key points of the tutorial include: - Characterisation of language in social media, and why it is difficult to process - In-depth examination of multiple approaches to core NLP tasks on social media text - Discussion of corpus collection and the use of crowdsourcing for annotation - Practical, legal and ethical aspects of gathering and distributing social media data and metadata - Current and future applications of social media information The tutorial takes a detailed view of key NLP tasks (corpus annotation, linguistic pre-processing, information extraction and opinion mining) of social media content. After a short introduction to the challenges of processing social media, we will cover key NLP algorithms adapted to processing such content, discuss available evaluation datasets and outline remaining challenges. The core of the tutorial will present NLP techniques tailored to social media, specifically: language identification, tokenisation, normalisation, part-of-speech tagging, named entity recognition, entity linking, event recognition, opinion mining, and text summarisation. Since the lack of human-annotated NLP corpora of social media content is another major challenge, this tutorial will cover also crowdsourcing approaches used to collect training and evaluation data (including paid-for crowdsourcing with CrowdFlower, also combined with expert-sourcing and games with a purpose). We will also discuss briefly practical and ethical considerations, arising from gathering and mining social media content. The last part of the tutorial will address applications, including summarisation of social media content, user modelling (geo-location, age, gender, and personality identification), media monitoring and information visualisation (for e.g. detecting bushfires, predicting virus outbreaks), and using social media to predict economical and political outcomes (e.g. stock price movements, voting intentions). Web address: http://eacl2014.org/tutorial-social-media Registration is to be made online via the EACL main registration site: http://eacl2014.org/registration This tutorial is supported by the CHIST-ERA project uComp (www.ucomp.eu) and also by the EU FP7 project Pheme (www.pheme.eu). Hope to see you in G?teborg! Leon Derczynski and Kalina Bontcheva From lila.kari at uwo.ca Wed Mar 5 10:20:10 2014 From: lila.kari at uwo.ca (Lila Kari) Date: Wed, 05 Mar 2014 10:20:10 -0500 Subject: Connectionists: UCNC 2014 - Extended Deadline Message-ID: <33F249EC-047D-4CDC-9D89-A9257F561CB3@uwo.ca> Due to multiple requests received, the UCNC paper submission deadline has been extended to March 14, 2014. UCNC 2014 The 13th International Conference on Unconventional Computation & Natural Computation University of Western Ontario, London, Ontario, Canada July 14-18, 2014 http://www.csd.uwo.ca/ucnc2014 http://www.facebook.com/UCNC2014 https://twitter.com/UCNC2014 Important Dates NEW Submission deadline: March 14, 2014 NEW Notification of acceptance: April 14, 2014 NEW Final version due: May 1st, 2014 From vcutsuridis at gmail.com Wed Mar 5 07:07:01 2014 From: vcutsuridis at gmail.com (Vassilis Cutsuridis) Date: Wed, 5 Mar 2014 14:07:01 +0200 Subject: Connectionists: Frontiers Research Topic: Memory Processes in Medial Temporal Lobe: Experimental, Theoretical and Computational Approaches Message-ID: Dear colleagues, we would like to inform you that our Research Topic organized with Frontiers in Neuroscience (Host Specialty: Frontiers in Systems Neuroscience) is still open and it will be accepting abstracts till April 1st, 2014. Our Research Topic is entitled: "Memory Processes in Medial Temporal Lobe: Experimental, Theoretical and Computational Approaches" Topic Editors: Motoharu Yoshida (University of Bochum) and Vassilis Cutsuridis (Foundation for Research and Technology - Hellas (FORTH)) Important deadlines ---------------------------- Abstract Submission Deadline: April 1st 2014 Article Submission Deadline: August 1st, 2014 Research Topic Description ---------------------------------------- The medial temporal lobe (MTL) includes the hippocampus, amygdala and parahippocampal regions, and is crucial for episodic and spatial memory. MTL memory function consists of distinct processes such as encoding, consolidation and retrieval. Encoding is the process by which perceived information is transformed into a memory trace. After encoding, memory traces are stabilized by consolidation. Memory retrieval (recall) refers to the process by which memory traces are reactivated to access information previously encoded and stored in the brain. Although underlying neural mechanisms supporting these distinct functional stages remain largely unknown, recent studies have indicated that distinct oscillatory dynamics, specific neuron types, synaptic plasticity and neuromodulation, play a central role. The theta rhythm is believed to be crucial in the encoding and retrieval of memories. Experimental and computational studies indicate that precise timing of principal cell firing in the hippocampus, relative to the theta rhythm, underlies encoding and retrieval processes. On the other hand, sharp-wave ripples have been implicated in the consolidation through the "replay" of memories in compressed time scales. The neural circuits and cell types supporting memory processes in the MTL areas have only recently been delineated using experimental approaches such as optogenetics, juxtacellular recordings and optical imaging. Principal (excitatory) cells are crucial for encoding, storing and retrieving memories at the cellular level, whereas inhibitory interneurons provide the temporal structures for orchestrating the activities of neuronal populations of principal cells by regulating synaptic integration and timing of action potential generation of principal cells as well as the generation and maintenance of network oscillations (rhythms). In addition, neuromodulators such as acetylcholine alter dynamical properties of neurons and synapses, and modulate oscillatory state and rules of synaptic plasticity and their levels might tune MTL to specific memory processes. The goal of the research topic is to offer a snapshot of the current stateof-the-art on how memories are encoded, consolidated, stored and retrieved in MTL structures. Particularly welcome will be studies (experimental or computational) focusing on the structure and function of neural circuits, their cellular components (principal cell and inhibitory interneurons), synaptic plasticity rules involved in these memory processes, network oscillations such as theta and sharp-wave ripples, and role of neuromodulators. Possible questions are: (1) Which areas or pathways within the MTL support encoding/consolidation/retrieval? (2) What neural activity defines specific memory processes? (3) What are the roles of neuromodulators in defining/switching these memory processes? (4) Could the role of synaptic plasticity be different in different memory processes? (5) What functional roles do the various inhibitory interneurons support during the encoding/consolidation/retrieval processes? About Frontiers Research Topics ------------------------------------------------ Frontiers Research Topics are designed to be an organized, encyclopedic coverage of a particular research area, and a forum for discussion and debate. Contributions can be of different article types (Original Research, Methods, Hypothesis & Theory, and others). Our Research Topic has a dedicated homepage on the Frontiers website, where contributing articles are accumulated and discussions can be easily held. Once all articles are published, the topic will be compiled into an e-book, which can be sent to foundations that fund your research, to journalists and press agencies, and to any number of other organizations. As the ultimate reference source from leading scientists, Frontiers Research Topic articles become highly cited. Frontiers is a Swiss-based, open access publisher. As such an article accepted for publication incurs a publishing fee, which varies depending on the article type. The publishing fee for accepted articles is below average compared to most other open access journals - and lower than subscription-based journals that apply page and color figure charges. Moreover, for Research Topic articles, the publishing fee is discounted quite steeply thanks to the support of the Frontiers Research Foundation. Details on Frontiers' fees can be found at http://www.frontiersin.org/about/PublishingFees. When published, your article will be freely available to visitors to the Frontiers site, and will be indexed in PubMed and other academic archives. As an author in Frontiers, you will retain the copyright to your own paper and all figures. The details regarding this Research Topic for Frontiers in Systems Neuroscience can be found at the following URL: http://www.frontiersin.org/systems_neuroscience/researchtopics/memory_processes_in_medial_tem_1/2540 Should you choose to participate, please confirm by sending a quick email and then your abstract using the following link: http://www.frontiersin.org/submissioninfo Thanks in advance for your interest! Vassilis Cutsuridis and Motoharu Yoshida --- Vassilis Cutsuridis, PhD IMBB - FORTH Heraklion, Crete Greece -------------- next part -------------- An HTML attachment was scrubbed... URL: From grlmc at urv.cat Wed Mar 5 16:45:06 2014 From: grlmc at urv.cat (GRLMC - URV) Date: Wed, 5 Mar 2014 22:45:06 +0100 Subject: Connectionists: SSTiC 2014: 15 March, 4th registration deadline Message-ID: <003a01cf38bc$30d8dec0$6400a8c0@GRLMC.local> *To be removed from our mailing list, please respond to this message with UNSUBSCRIBE in the subject line* ********************************************************************* 2014 TARRAGONA INTERNATIONAL SUMMER SCHOOL ON TRENDS IN COMPUTING SSTiC 2014 Tarragona, Spain July 7-11, 2014 Organized by Rovira i Virgili University http://grammars.grlmc.com/sstic2014/ ********************************************************************* --- March 15, 4th registration deadline --- ********************************************************************* AIM: SSTiC 2014 is the second edition in a series started in 2013. For the previous event, see http://grammars.grlmc.com/SSTiC2013/ SSTiC 2014 will be a research training event mainly addressed to PhD students and PhD holders in the first steps of their academic career. It intends to update them about the most recent developments in the diverse branches of computer science and its neighbouring areas. To that purpose, renowned scholars will lecture and will be available for interaction with the audience. SSTiC 2014 will cover the whole spectrum of computer science through 6 keynote lectures and 24 six-hour courses dealing with some of the most lively topics in the field. The organizers share the idea that outstanding speakers will really attract the brightest students. ADDRESSED TO: Graduate students from around the world. There are no formal pre-requisites in terms of the academic degree the attendee must hold. However, since there will be several levels among the courses, reference may be made to specific knowledge background in the description of some of them. SSTiC 2014 is also appropriate for more senior people who want to keep themselves updated on developments in their own field or in other branches of computer science. They will surely find it fruitful to listen and discuss with scholars who are main references in computing nowadays. REGIME: In addition to keynotes, 3 parallel sessions will be held during the whole event. Participants will be able to freely choose the courses they will be willing to attend as well as to move from one to another. VENUE: SSTiC 2014 will take place in Tarragona, located 90 kms. to the south of Barcelona. The venue will be: Campus Catalunya Universitat Rovira i Virgili Av. Catalunya, 35 43002 Tarragona KEYNOTE SPEAKERS: Larry S. Davis (U Maryland, College Park), A Historical Perspective of Computer Vision Models for Object Recognition and Scene Analysis David S. Johnson (Columbia U, New York), Open and Closed Problems in NP-Completeness George Karypis (U Minnesota, Twin Cities), Recommender Systems Past, Present, & Future Steffen Staab (U Koblenz), Explicit and Implicit Semantics: Two Sides of One Coin Philip Wadler (U Edinburgh), You and Your Research and The Elements of Style Ronald R. Yager (Iona C, New Rochelle), Social Modeling COURSES AND PROFESSORS: Divyakant Agrawal (U California, Santa Barbara), [intermediate] Scalable Data Management in Enterprise and Cloud Computing Infrastructures Pierre Baldi (U California, Irvine), [intermediate] Big Data Informatics Challenges and Opportunities in the Life Sciences Rajkumar Buyya (U Melbourne), [intermediate] Cloud Computing John M. Carroll (Pennsylvania State U, University Park), [introductory] Usability Engineering and Scenario-based Design Kwang-Ting (Tim) Cheng (U California, Santa Barbara), [introductory/intermediate] Smartphones: Hardware Platform, Software Development, and Emerging Apps Amr El Abbadi (U California, Santa Barbara), [introductory] The Distributed Foundations of Data Management in the Cloud Richard M. Fujimoto (Georgia Tech, Atlanta), [introductory] Parallel and Distributed Simulation Mark Guzdial (Georgia Tech, Atlanta), [introductory] Computing Education Research: What We Know about Learning and Teaching Computer Science David S. Johnson (Columbia U, New York), [introductory] The Traveling Salesman Problem in Theory and Practice George Karypis (U Minnesota, Twin Cities), [intermediate] Programming Models/Frameworks for Parallel & Distributed Computing Aggelos K. Katsaggelos (Northwestern U, Evanston), [intermediate] Optimization Techniques for Sparse/Low-rank Recovery Problems in Image Processing and Machine Learning Arie E. Kaufman (U Stony Brook), [advanced] Visualization Carl Lagoze (U Michigan, Ann Arbor), [introductory] Curation of Big Data Dinesh Manocha (U North Carolina, Chapel Hill), [introductory/intermediate] Robot Motion Planning Bijan Parsia (U Manchester), [introductory] The Empirical Mindset in Computer Science Charles E. Perkins (FutureWei Technologies, Santa Clara), [intermediate] Beyond LTE: the Evolution of 4G Networks and the Need for Higher Performance Handover System Designs Sudhakar M. Reddy (U Iowa, Iowa City), [introductory] Test and Design for Test of Digital Logic Circuits Robert Sargent (Syracuse U), [introductory] Validation of Models Mubarak Shah (U Central Florida, Orlando), [intermediate] Visual Crowd Analysis Steffen Staab (U Koblenz), [intermediate] Programming the Semantic Web Mike Thelwall (U Wolverhampton), [introductory] Sentiment Strength Detection for Twitter and the Social Web Jeffrey D. Ullman (Stanford U), [introductory] MapReduce Algorithms Nitin Vaidya (U Illinois, Urbana-Champaign), [introductory/intermediate] Distributed Consensus: Theory and Applications Philip Wadler (U Edinburgh), [intermediate] Topics in Lambda Calculus and Life ORGANIZING COMMITTEE: Adrian Horia Dediu (Tarragona) Carlos Mart?n-Vide (Tarragona, chair) Florentina Lilica Voicu (Tarragona) REGISTRATION: It has to be done at http://grammars.grlmc.com/sstic2014/registration.php The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an approximation of the respective demand for each course. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed when the capacity of the venue will be complete. It is very convenient to register prior to the event. FEES: As far as possible, participants are expected to attend for the whole (or most of the) week (full-time). Fees are a flat rate allowing one to participate to all courses. They vary depending on the registration deadline. ACCOMMODATION: Information about accommodation is available on the website of the School. CERTIFICATE: Participants will be delivered a certificate of attendance. QUESTIONS AND FURTHER INFORMATION: florentinalilica.voicu at urv.cat POSTAL ADDRESS: SSTiC 2014 Lilica Voicu Rovira i Virgili University Av. Catalunya, 35 43002 Tarragona, Spain Phone: +34 977 559 543 Fax: +34 977 558 386 ACKNOWLEDGEMENTS: Departament d?Economia i Coneixement, Generalitat de Catalunya Universitat Rovira i Virgili From ralph.etiennecummings at gmail.com Wed Mar 5 21:28:35 2014 From: ralph.etiennecummings at gmail.com (Ralph Etienne-Cummings) Date: Wed, 5 Mar 2014 21:28:35 -0500 Subject: Connectionists: Deadline Approaching: Telluride Neuromorphic Cognition Engineering Workshop Message-ID: Telluride Neuromorphic Cognition Engineering Workshop 2014 Neuromorphic Cognition Engineering Workshop: The 20th Anniversary Edition Telluride, Colorado, June 29th - July 19th, 2014CALL FOR APPLICATIONS: Deadline is April 2nd, 2014 NEUROMORPHIC COGNITION ENGINEERING WORKSHOP www.ine-web.org Sunday June 29th - Saturday July 19th, 2014, Telluride, Colorado We invite applications for a three-week summer workshop that will be held in Telluride, Colorado. Sunday June 29th - Saturday July 19th, 2014. The application deadline is Wednesday, April 2nd and application instructions are described at the bottom of this document. This is the 20th Anniversary of the Workshop, and ~25 years since the conception of the "Meadian" version of Neuromorphic Engineering. Hence, we plan a celebratory Workshop, where some of the originators and benefactors of the field will participate in discussions of the successes and challenges over the past 25 years and prognosticate the potential contributions for the next 25 years. The 2014 Workshop and Summer School on Neuromorphic Engineering is sponsored by the National Science Foundation, Institute of Neuromorphic Engineering, Qualcomm Corporation, The EU-Collaborative Convergent Science Network (CNS-II), University of Maryland - College Park, Institute for Neuroinformatics - University of Zurich and ETH Zurich, Georgia Institute of Technology, Johns Hopkins University, Boston University, University of Western Sydney and the Salk Institute. Directors: - Cornelia Fermuller, University of Maryland, College Park - Ralph Etienne-Cummings, Johns Hopkins University - Shih-Chii Liu, Institute of Neuroinformatics, UNI/ETH Zurich, Switzerland - Timothy Horiuchi, University of Maryland, College Park Workshop Advisory Board: - Andreas Andreou, Johns Hopkins University - Andre van Schaik, University Western Sydney, Australia - Avis Cohen, University of Maryland - Barbara Shinn-Cunningham, Boston University - Giacomo Indiveri, Institute of Neuroinformatics, Uni/Eth Zurich, Switzerland - Jonathan Tapson, University Western Sydney, Australia - Malcolm Slaney, Microsoft Research - Jennifer Hasler, Georgia Institute of Technology - Rodney Douglas, Institute of Neuroinformatics, Uni/Eth Zurich, Switzerland - Shihab Shamma, University of Maryland - Tobi Delbruck, Institute of Neuroinformatics, Uni/Eth Zurich, Switzerland Previous year workshop can be found at: http://ine-web.org/workshops/workshops-overview/index.html and the workshop wiki is athttps://neuromorphs.net/ GOALS: Neuromorphic engineers design and fabricate artificial neural systems whose organizing principles are based on those of biological nervous systems. Over the past 18 years, this research community has focused on the understanding of low-level sensory processing and systems infrastructure; efforts are now expanding to apply this knowledge and infrastructure to addressing higher-level problems in perception, cognition, and learning. In this 3-week intensive workshop and through the Institute for Neuromorphic Engineering (INE), the mission is to promote interaction between senior and junior researchers; to educate new members of the community; to introduce new enabling fields and applications to the community; to promote on-going collaborative activities emerging from the Workshop, and to promote a self-sustaining research field. FORMAT: The three week summer workshop will include background lectures on systems and cognitive neuroscience (in particular sensory processing, learning and memory, motor systems and attention), practical tutorials on emerging hardware design, mobile robots, hands-on projects, and special interest groups. Participants are required to take part and possibly complete at least one of the projects proposed. They are furthermore encouraged to become involved in as many of the other activities proposed as interest and time allow. There will be two lectures in the morning that cover issues that are important to the community in general. Because of the diverse range of backgrounds among the participants, some of these lectures will be tutorials, rather than detailed reports of current research. These lectures will be given by invited speakers. Projects and interest groups meet in the late afternoons, and after dinner. In the early afternoon there will be tutorials on a wide spectrum of topics, including analog VLSI, mobile robotics, vision and auditory systems, central-pattern-generators, selective attention mechanisms, cognitive systems, etc. *2014 TOPIC AREAS:* 1. *Human Auditory Cognition: Acoustic Priming, Imagination and Attention.* Project Leaders: Shihab Shamma (UM-College Park), Malcolm Slaney (Microsoft), Edward Lalor (Trinity College, Dublin), Barbara Shinn-Cunningham (Boston U) 2. *Motion and Action Processing on Wearable Devices* Project Leaders: Michael Pfeiffer (INI-UZH), Ryad Benosman (UPMC, Paris), Garrick Orchard (NUS, Singapore), and Cornelia Ferm?ller (UMCP) 3. *Planning with Dynamic Neural Fields: from Sensorimotor Dynamics to Large-Scale behavioral Search* Project Leaders: Yulia Sandamirskaya (RUB, Bochum) and Erik Billing (U. Skovde) 4. *Neuromorphic Olympics* Project Leaders: Jorg Conradt (TUM, Munich) and Terry Stewart (U. Waterloo) 5. *Embodied Neuromorphic Real-World Architectures of Perception, Cognition and Action* Project Leaders: Andreas Andreou (JHU) and Paul Verschure (UPF, Barcelona) 6. *Terry Sejnowski (Salk Institute) - Computational Neuroscience (invitational mini-workshop)* LOCATION AND ARRANGEMENTS: The summer school will take place in the small town of Telluride, 9000 feet high in southwest Colorado, about 6 hours drive away from Denver (350 miles). Great Lakes Aviation and America West Express airlines provide daily flights directly into Telluride. All facilities within the beautifully renovated public school building are fully accessible to participants with disabilities. Participants will be housed in ski condominiums, within walking distance of the school. Participants are expected to share condominiums. The workshop is intended to be very informal and hands-on. Participants are not required to have had previous experience in analog VLSI circuit design, computational or machine vision, systems level neurophysiology or modeling the brain at the systems level. However, we strongly encourage active researchers with relevant backgrounds from academia, industry and national laboratories to apply, in particular if they are prepared to work on specific projects, talk about their own work or bring demonstrations to Telluride (e.g. robots, chips, software). Wireless internet access will be provided. Technical staff present throughout the workshops will assist with software and hardware issues. We will have a network of PCs running LINUX and Microsoft Windows for the workshop projects. We encourage participants to bring along their personal laptop. No cars are required. Given the small size of the town, we recommend that you do not rent a car. Bring hiking boots, warm clothes, rain gear, and a backpack, since Telluride is surrounded by beautiful mountains. Unless otherwise arranged with one of the organizers, we expect participants to stay for the entire duration of this three week workshop. FINANCIAL ARRANGEMENTS: Notification of acceptances will be mailed out around the April 15th, 2014. The Workshop covers all your accommodations and facilities costs for the 3 weeks duration. You are responsible for your own travel to the Workshop, however, sponsored fellowships will be available as described below to further subsidize your cost. Registration Fees: For expenses not covered by federal funds, a Workshop registration fee is required. The fee is $1250 per participant for the 3-week Workshop. This is expected from all participants at the time of acceptance. Accommodations: The cost of a shared condominium, typically a bedroom in a shared condo for senior participants or a shared room for students, will be covered for all academic participants. Upgrades to a private rooms or condos will cost extra. Participants from National Laboratories and Industry are expected to pay for these condominiums. Fellowships: This year we will offer two Fellowships to subsidize your costs: 1. Qualcomm Corporation Fellowship: Three non-corporate participants will have their accommodation and registration fees ($2750) directly covered by Qualcomm, and will be reimbursed for travel costs up to $500. Additional generous funding from Qualcomm will provide $5000 to help organize and stage the Workshop. 2. EU-CSNII Fellowship (http://csnetwork.eu/) which is funded by the 7th Research Framework Program FP7-ICT-CSNII-601167: The top 8 EU applicants will be reimbursed for their registration fees ($1250), subsistence/travel subsidy (up to Euro 2000) and accommodations cost ($1500). The registration and accommodation costs will go directly to the INE (the INE will reimburse the participant's registration fees after receipt from CSNII), while the subsistence/travel reimbursement will be provided directly to the participants by the CSNII at the University of Pompeu Fabra, Barcelona, Spain. HOW TO APPLY: Applicants should be at the level of graduate students or above (i.e. postdoctoral fellows, faculty, research and engineering staff and the equivalent positions in industry and national laboratories). We actively encourage women and minority candidates to apply. Anyone interested in proposing or discussing specific projects should contact the appropriate topic leaders directly. The application website is (after February 7th, 2014): ine-web.org/telluride-conference-2014/apply-info Application information needed: - Contact email address. - First name, Last name, Affiliation, valid e-mail address. - Curriculum Vitae (a short version, please). - One page summary of background and interests relevant to the workshop, including possible ideas for workshop projects. Please indicate which topic areas you would most likely join. - Two letters of recommendation (uploaded directly by references). *Applicants will be notified by e-mail.* 7th February, 2014 - Applications accepted on website 2nd April, 2014 - Applications Due 15th April, 2014 - Notification of Acceptance ------------------------- -- Ralph Etienne-Cummings, PhD, FIEEE Professor Department of Electrical and Computer Engineering Computational Sensor Motor Systems Lab -------------- next part -------------- An HTML attachment was scrubbed... URL: From pbrazdil at inescporto.pt Thu Mar 6 07:31:49 2014 From: pbrazdil at inescporto.pt (Pavel Brazdil) Date: Thu, 6 Mar 2014 12:31:49 +0000 Subject: Connectionists: ECAI-14 Workshop and Tutorial on Metalearning & Algorithm Selection Message-ID: MetaSel - Meta-learning & Algorithm Selection ********************************************* ECAI-2014 Workshop, Prague, 19 August 2014 (date altered) http://metasel2014.inescporto.pt/ Announcement & Call for Papers Objectives This ECAI-2014 workshop will provide a platform for discussing the nature of algorithm selection which arises in many diverse domains, such as machine learning, data mining, optimization and satisfiability solving, among many others. Algorithm Selection and configuration are increasingly relevant today. Researchers and practitioners from all branches of science and technology face a large choice of parameterized machine learning algorithms, with little guidance as to which techniques to use. Moreover, data mining challenges frequently remind us that algorithm selection and configuration are crucial in order to achieve the best performance, and drive industrial applications. Meta-learning leverages knowledge of past algorithm applications to select the best techniques for future applications, and offers effective techniques that are superior to humans both in terms of the end result and especially in the time required to achieve it. In this workshop we will discuss different ways of exploiting meta-learning techniques to identify the potentially best algorithm(s) for a new task, based on meta-level information and prior experiments. We also discuss the prerequisites for effective meta-learning systems such as recent infrastructure such as OpenML.org. Many problems of today require that solutions be elaborated in the form of complex systems or workflows which include many different processes or operations. Constructing such complex systems or workflows requires extensive expertise, and could be greatly facilitated by leveraging planning, meta-learning and intelligent system design. This task is inherently interdisciplinary, as it builds on expertise in various areas of AI. The workshop will include invited talks, presentations of peer-reviewed papers and panels. The invited talks will be by Lars Kotthoff and Frank Hutter (to be confirmed). The target audience of this workshop includes researchers (Ph.D.'s) and research students interested to exchange their knowledge about: - problems and solutions of algorithm selection and algorithm configuration - how to use software and platforms to select algorithms in practice - how to provide advice to end users about which algorithms to select in diverse domains, including optimization, SAT etc. and incorporate this knowledge in new platforms. We specifically aim to attract researchers in diverse areas that have encountered the problem of algorithm selection and thus promote exchange of ideas and possible collaborations. Topics Algorithm Selection & Configuration Planning to learn and construct workflows Applications of workflow planning Meta-learning and exploitation of meta-knowledge Exploitation of ontologies of tasks and methods Exploitation of benchmarks and experimentation Representation of learning goals and states in learning Control and coordination of learning processes Meta-reasoning Experimentation and evaluation of learning processes Layered learning Multi-task and transfer learning Learning to learn Intelligent design Performance modeling Process mining Submissions and Review Process Important dates: Submission deadline: 25 May 2014 Notification: 23 June 2014 Full papers can consist of a maximum of 8 pages, extended abstracts up to 2 pages, in the ECAI format. Each submission must be submitted online via the Easychair submission interface. Submissions can be updated at will before the submission deadline. Electronic versions of accepted submissions will also be made publicly available on the conference web site. The only accepted format for submitted papers is PDF. Submissions are possible either as a full paper or as an extended abstract. Full papers should present more advanced work, covering research or a case application. Extended abstracts may present current, recently published or future research, and can cover a wider scope. For instance, they may be position statements, offer a specific scientific or business problem to be solved by machine learning (ML) / data mining (DM) or describe ML / DM demo or installation. Each paper submission will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least two members of the program committee. All accepted submissions will be included in the conference proceedings. At least one author of each accepted full paper or extended abstract is required to attend the workshop to present the contribution. A selection will be made of the best paper and runner ups, and these will be presented in the plenary session. The remainder of accepted submissions will be presented in the form of short talks and a poster session. All accepted papers, including those presented as a poster, will be published in the workshop proceedings (possibly as CEUR Workshop Proceedings). The papers selected for plenary presentation will be identified in the proceedings. Organizers: Pavel Brazdil, FEP, Univ. of Porto / Inesc Tec, Portugal, pbrazdil at inescporto.pt Carlos Soares, FEUP, Univ. of Porto / Inesc Tec, Portugal, csoares at fe.up.pt Joaquin Vanschoren, Eindhoven University of Technology (TU/e), Eindhoven, The Netherlands, j.vanschoren at tue.nl Lars Kotthoff, University College Cork, Cork, Ireland, larsko at 4c.ucc.ie Program Committee: Pavel Brazdil, LIAAD-INESC Porto L.A. / FEP, University of Porto, Portugal Andr? C. P. Carvalho, USP, Brasil Claudia Diamantini, Universit? Politecnica delle Marche, Italy Johannes Fuernkranz, TU Darmstadt, Germany Christophe Giraud-Carrier, Brigham Young Univ., USA Krzysztof Grabczewski, Nicolaus Copernicus University, Poland Melanie Hilario, Switzerland Frank Hutter, University of Freiburg, Germany Christopher Jefferson, University of St Andrews, UK Alexandros Kalousis, U Geneva, Switzerland J?rg-Uwe Kietz, U.Zurich, Switzerland Lars Kotthoff, University College Cork, Ireland Yuri Malitsky, University College Cork, Ireland Bernhard Pfahringer, U Waikato, New Zealand Vid Podpecan, Jozef Stefan Institute, Slovenia Ricardo Prud?ncio, Univ. Federal de Pernambuco Recife (PE), Brasil Carlos Soares, FEP, University of Porto, Portugal Guido Tack, Monash University, Australia Joaquin Vanschoren, U. Leiden / KU Leuven Ricardo Vilalta, University of Houston, USA Filip Zelezn?, CVUT, Prague, R.Checa Previous events This workshop is closely related to the PlanLearn-2012, which took place at ECAI-2012 and other predecessor workshops in this series. Tutorial on Metalearning and Algorithm Selection at ECAI-2014 ************************************************************* 18 August 2014 http://metasel.inescporto.pt/ Algorithm Selection and configuration are increasingly relevant today. Researchers and practitioners from all branches of science and technology face a large choice of parameterized algorithms, with little guidance as to which techniques to use. Moreover, data mining challenges frequently remind us that algorithm selection and configuration are crucial in order to achieve the best performance and drive industrial applications. Meta-learning leverages knowledge of past algorithm applications to select the best techniques for future applications, and offers effective techniques that are superior to humans both in terms of the end result and especially in a limited time. In this tutorial, we elucidate the nature of algorithm selection and how it arises in many diverse domains, such as machine learning, data mining, optimization and SAT solving. We show that it is possible to use meta-learning techniques to identify the potentially best algorithm(s) for a new task, based on meta-level information and prior experiments. We also discuss the prerequisites for effective meta-learning systems, and how recent infrastructures, such as OpenML.org, allow us to build systems that effectively advice users on which algorithms to apply. The intended audience includes researchers (Ph.D.'s), research students and practitioners interested to learn about, or consolidate their knowledge about the state-of-the-art in algorithm selection and algorithm configuration, how to use Data Mining software and platforms to select algorithms in practice, how to provide advice to end users about which algorithms to select in diverse domains, including optimization, SAT etc. and incorporate this knowledge in new platforms. The participants should bring their own laptops. From sen.cheng at rub.de Thu Mar 6 02:34:49 2014 From: sen.cheng at rub.de (Sen Cheng) Date: Thu, 6 Mar 2014 08:34:49 +0100 Subject: Connectionists: Fwd: European Summer School: "Memory and Mind: Perspectives from Philosophy and Neuroscience" In-Reply-To: References: Message-ID: European Summer School: "Memory and Mind: Perspectives from Philosophy and Neuroscience" Dates: September 7th, 2014 to September 20th, 2014 Venue: Ruhr-University Bochum, Germany About the Event The demands on young researchers in philosophy and the cognitive and neurosciences have increased dramatically during the last decade. Whereas many philosophers need to understand state-of-the-art empirical research in the cognitive and neurosciences, empirical scientists often face conceptual challenges which require philosophical knowledge and excellent analytic skills. From 7th Sept ? 20th Sept 2014, the European Campus of Excellence summer school will be held at the Ruhr University Bochum, taking a truly integrative approach to contemporary problems in the areas of memory and mind. Gifted students from all over Europe meet with world-class scholars from all over the world to learn from each other, talk over and conduct research together. The young academics not only get the opportunity to deepen their knowledge on questions of memory and mind but also to get to know fellow students from other European universities. The summer school in Bochum is divided into four subsequent theoretical as well as four partly parallel experimental sections. Each theoretical section is dedicated to one core issue of research into the structure of memory and mind. This summer school is supported by Stiftung Mercator and the Hertie Foundation. It is an initiative in the framework of the European Campus of Excellence (ECE), organised by Ruhr University Bochum Scientific Chairs: Prof. Dr. Markus Werning, Prof. Dr. Magdalena Sauvage, Prof. Dr. Sen Cheng, Prof. Dr. Boris Suchan, Dr. Kevin Reuter Confirmed Speakers: Prof. Dr. Allesio Avenanti (Bologna, Psychology), Prof. Dr. Jan Born (T?bingen, Neuroendocrinology), Prof. Dr. John Campbell (Berkeley (Ca), Philosophy), Prof. Dr. Carl Craver (St Louis, Philosophy), Prof. Dr. Jozsef Csicsvari (IST, Klosterneuburg, Neuropharmacology), Prof. Dr. Ronald de Sousa (Toronto, Philosophy), Prof. Dr. Alain Destexhe (CNRS, Gif-sur-Yvette, Neuroscience), Prof. Dr. Kim Graham (Cardiff, Psychology), Prof. Dr. Michael Hasselmo (Boston University, Psychological & Brain Sciences), Prof. Dr. Lynn Nadel (Arizona, Psychology), Prof. Dr. Karim Nader (McGill, Neuroscience), Prof. Albert Newen (Bochum, Philosophy) Prof. Dr. Charan Ranganath (UC Davis, Psychology), Prof. Dr. Craig Stark (Irvine (Ca), Neurobiology), Dr. Jennifer Windt (Mainz, Philosophy). Scholarships: We are committed to admitting talented students from diverse backgrounds. Accepted applicants will receive a scholarship: tuition will be waved; travel, lodging and food will be mostly covered. Application: The online application period for this summer school will open on 15 February 2014 and close on 20 April 2014. Contact: Kevin Reuter Email:kevin.reuter at rub.de Website: http://euca-excellence.eu/bochum2014 From S.J.Eglen at damtp.cam.ac.uk Thu Mar 6 07:09:31 2014 From: S.J.Eglen at damtp.cam.ac.uk (Stephen Eglen) Date: Thu, 06 Mar 2014 12:09:31 +0000 Subject: Connectionists: From Maps to Circuits: Models and Mechanisms for Generating Neural Connections Message-ID: <85ha7b34w4.fsf@damtp.cam.ac.uk> From Maps to Circuits: Models and Mechanisms for Generating Neural Connections 28/29 July 2014, Edinburgh UK http://maps2014.org Organisers: Stephen Eglen, Matthias Hennig, Andrew Huberman, David Sterratt, Ian Thompson, David Willshaw Aim of the meeting Understanding the development of the nervous system is a key challenge that has been approached by both experimental and theoretical neuroscientists. In recent years there has been a gradual move towards the two groups working more with each other. The idea of this workshop is to bring key people together who have shown an interest at combining theoretical and experimental techniques to discuss current problems in neuronal development, and plan future collaborative efforts. Time at the end of each day of the workshop will be devoted to a group discussion about questions that have been raised during the day to identify possible research directions and people willing to pursue them. Speakers: Tom Clandinin (Stanford), Michael Crair (Yale), Irina Erchova (Cardiff), David Feldheim (UC Santa Cruz), Geoffrey Goodhill (U Queensland), Robert Hindges (Kings College London), Sonja Hofer (Basel), Hitoshi Sakano (U Tokyo), David Wilkinson (NIMR, London), David Willshaw (Edinburgh), Fred Wolf (Gottingen). This meeting is supported by Cambridge Univeristy Press, Company of Biologists, Gatsby Charitable Foundation, Institute for Adaptive and Neural Computation, Wellcome Trust. From g.westermann at lancaster.ac.uk Thu Mar 6 10:22:11 2014 From: g.westermann at lancaster.ac.uk (Gert Westermann) Date: Thu, 6 Mar 2014 15:22:11 +0000 Subject: Connectionists: 14th Neural Computation and Psychology Workshop (NCPW14) call for papers Message-ID: <7107702F-C3F3-443A-AF4D-A14FDF1AEFF2@lancaster.ac.uk> Dear colleague, We cordially invite you to participate in the 14th Neural Computation and Psychology Workshop (NCPW14) to be held at Lancaster University, UK, from August 21-23, 2014: http://www.psych.lancs.ac.uk/ncpw14 This well-established and lively workshop aims at bringing together researchers from different disciplines such as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their work on models of cognitive processes. Often this workshop has had a theme, and this time it is ?Development across the lifespan?, but also submissions that do not fall under this theme are welcome. Papers must be about emergent models - frequently, but not necessarily - of the connectionist/neural network kind, applied to cognition. The NCPW workshops have always been characterized by their limited size, high quality papers, the absence of parallel talk sessions, and a schedule that is explicitly designed to encourage interaction among the researchers present in an informal setting. NCPW14 is no exception. The scientific program will consist of keynote lectures, oral sessions and poster sessions. Furthermore, this workshop will feature a unique set of invited speakers: James McClelland, Stanford University. Bob McMurray, University of Iowa. Michael Thomas, Birkbeck College, London. Abstract submission Please submit one-page abstracts (pdf) by email to ncpw14conf at gmail.com, stating whether you want to present as a talk, poster, or talk/poster. Rumelhart Memorial Travel Awards The Rumelhart Memorial Travel awards, generously funded by Professor Jay McClelland, will provide funding to support travel costs for students/post docs presenting at the conference. Awards of US$250 are available to students or post docs from Western European countries, and US$750 for students or post docs from elsewhere. Decisions will be based on the quality of the submission. Eligibility criteria: The first author of the submission is a PhD student or Post-doctoral fellow who will attend the meeting and will present the submission if chosen to receive a travel award. Location Lancaster is situated in the north west of England, approximately one hour from Manchester and Liverpool airports. Lancaster is surrounded by spectacular scenery, hiking and climbing country. The Yorkshire Dales national park is 10 miles away. The Lake District national park is 20 miles away, http://www.nationalparks.gov.uk. The stagecoach 555 bus (http://www.stagecoachbus.com) takes you directly from Lancaster through the heart of the Lakes. The Trough of Bowland Area of Outstanding Natural Beauty is 2 miles away. Important dates to remember Abstract deadline: 15 May Notification of abstract acceptance: 7 June Early registration deadline: tbc Online registration deadline: tbc Conference dates: 21-23 August, 2014 Looking forward to your participation! Organizing Committee Gert Westermann, Lancaster University Padraic Monaghan, Lancaster University Katherine E. Twomey, Liverpool University Alastair C. Smith, MPI Nijmegen -------------------------------------------------------------------------- Prof. Gert Westermann Department of Psychology Lancaster University Lancaster LA1 4YF Phone: +44 (0)1524 592 942 Fax: +44 (0)1524 593 744 g.westermann at lancaster.ac.uk http://www.psych.lancs.ac.uk/people/gert-westermann -------------- next part -------------- An HTML attachment was scrubbed... URL: From camda at bioinf.jku.at Thu Mar 6 07:31:56 2014 From: camda at bioinf.jku.at (CAMDA) Date: Thu, 6 Mar 2014 13:31:56 +0100 Subject: Connectionists: Conference Announcement - CAMDA 2014 Message-ID: Dear Colleagues, With great pleasure we announce the 13th International Conference on the Critical Assessment of Massive Data Analysis (CAMDA) in Boston, MA, U.S.A., July 11-12, 2014, held as official Satellite Meeting to 22nd Conference on Intelligent Systems for Molecular Biology (ISMB). CAMDA focuses on innovative methods to analyze massive data sets from life sciences. Over two days, researchers from bioinformatics, computer sciences, statistics, genetics, molecular biology, and other fields present novel approaches to analyze Big Data. An essential part of CAMDA is its competitive challenge where big heterogeneous data sets are analyzed and outcomes and methods compared. Academic and industrial researchers worldwide are invited to take the CAMDA challenge, to show their expertise in handling Big Data, and to present their results. Submitted abstracts are selected for oral and poster presentations. As in last years, the prestigious CAMDA prize will be awarded for the best presentation. Selected submissions are published in the CAMDA Proceedings as an open access PubMed indexed special issue of Systems Biomedicine. You can find additional information about the challenge data sets, submissions, etc. at www.camda.info. Some key dates are: - Abstract submission deadline for oral presentation / 20 May 2014 - Abstract submission deadline for poster presentation / 25 May 2014 - Notification of accepted contributions / 30 May 2014 - Early registration closes / 1 June 2014 As in past years, contest presentations are complemented by high profile keynotes (with recent speakers including Sandrine Dudoit, Mark Gerstein, John Quackenbush, Terry Speed, John Storey, Eran Segal, Atul Butte, Nikolaus Rajewsky, and others). This year, we are delighted to welcome: Chris Sander, Memorial Sloan Kettering Cancer Center, NY, USA Temple F. Smith, Boston University, MA, USA Jun Wang, Beijing Genomics Institute (BGI), Shenzhen, China We look forward to seeing you in Boston! The organizers and chairs of CAMDA 2014 Chairs: Djork-Arn? Clevert, Johannes Kepler University, Austria Joaquin Dopazo, CIPF, Spain Sepp Hochreiter, Johannes Kepler University, Austria Lan Hu Dana-Farber Cancer Institute, Boston, MA, U.S.A. David Kreil, Boku University, Austria Simon Lin, Marshfield Clinic, U.S.A. Contact: camda at bioinf.jku.at Conference website: http://www.camda.info From c.hilgetag at gmail.com Fri Mar 7 04:50:13 2014 From: c.hilgetag at gmail.com (Claus C. Hilgetag) Date: Fri, 7 Mar 2014 10:50:13 +0100 Subject: Connectionists: Reminder: Brain Connectivity Workshop 2014 - Early registration closes 31 March References: <95D60C0D-332D-48BB-B4D2-10F6EF3914C1@gmail.com> Message-ID: This is a repeat announcement of the Brain Connectivity Workshop 2014 which will take place in Hamburg, Germany, from 4th to 6th June 2014 (welcome reception on the evening of 3rd June). See website (http://sfb936.net/index.php/events/brain-connectivity-workshop-2014) for details. The Brain Connectivity Workshop (http://www.brain-connectivity-workshop.org) is an annual meeting that is dedicated to discussing the latest approaches and findings in the field of brain connectivity studies within a small group of experts. Hence attendance is limited to 140 participants, and the workshop strongly aims to facilitate exchange and discussion of ideas by a number of unique features. Important dates at a glance: * Registration: Now open - early registration until March 31, 2014, http://www.sfb936.net/index.php/events/brain-connectivity-workshop-2014. Because of the limited number of participants, early registration is strongly advised; seats will be allocated on a first come, first served principle. Speakers do not have to register. * The HBM 2014 meeting will take place in Hamburg, from June 8th to 12th (http://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=3565), right after the Brain Connectivity Workshop. We look forward to seeing you, Prof. Claus C. Hilgetag, PhD (University Medical Center Hamburg-Eppendorf, Dept. of Computational Neuroscience) Prof. Dr. Klaas E. Stephan (University of Zurich (UZH) & Swiss Federal Institute of Technology (ETH) Zurich) Prof. Dr. Andreas K. Engel (University Medical Center Hamburg-Eppendorf, Dept. of Neurophysiology and Pathophysiology) Prof. Dr. Christian Gerloff (University Medical Center Hamburg-Eppendorf, Clinic and Policlinic of Neurology) Hilke Marina Petersen (University Medical Center Hamburg-Eppendorf, Dept. of Neurophysiology and Pathophysiology) ? Management The workshop is supported by DFG Sonderforschungsbereich 936 "Multi-Site Communication in the Brain" (SFB 936), www.sfb936.net. Apologies for cross-posting. -------------- next part -------------- An HTML attachment was scrubbed... URL: From n.lepora at sheffield.ac.uk Fri Mar 7 05:47:47 2014 From: n.lepora at sheffield.ac.uk (Nathan F Lepora) Date: Fri, 7 Mar 2014 10:47:47 +0000 Subject: Connectionists: [meetings] Living Machines III: Final Call for Papers, Satellite Events and Sponsors Message-ID: ______________________________________________________________ Final Call for Papers, Satellite Events and Sponsors Living Machines III: The 3rd International Conference on Biomimetic and Biohybrid Systems 30th July to 1st August 2014 http://csnetwork.eu/livingmachines/conf2014 To be hosted at the Museo Nazionale Della Scienza E Della Tecnologia Leonardo Da Vinci (National Museum of Science and Technology Leonardo da Vinci) Milan, Italy In association with the Istituto Italiano di Technologia (IIT) Accepted papers will be published in Springer Lecture Notes in Artificial Intelligence Submission deadline March 14th, 2014. Papers should be submitted through the Springer web-portal http://senldogo0039.springer-sbm.com/ocs/conference/submitpaperto/LM2014 ______________________________________________________________ ABOUT LIVING MACHINES 2014 The development of future real-world technologies will depend strongly on our understanding and harnessing of the principles underlying living systems and the flow of communication signals between living and artificial systems. Biomimetics is the development of novel technologies through the distillation of principles from the study of biological systems. The investigation of biomimetic systems can serve two complementary goals. First, a suitably designed and configured biomimetic artefact can be used to test theories about the natural system of interest. Second, biomimetic technologies can provide useful, elegant and efficient solutions to unsolved challenges in science and engineering. Biohybrid systems are formed by combining at least one biological component--an existing living system--and at least one artificial, newly-engineered component. By passing information in one or both directions, such a system forms a new hybrid bio-artificial entity. The following are some examples: * Biomimetic robots and their component technologies (sensors, actuators, processors) that can intelligently interact with their environments. * Active biomimetic materials and structures that self-organize and self-repair. * Biomimetic computers--neuromimetic emulations of the physiological basis for intelligent behaviour. * Biohybrid brain-machine interfaces and neural implants. * Artificial organs and body-parts including sensory organ-chip hybrids and intelligent prostheses. * Organism-level biohybrids such as robot-animal or robot-human systems. ACTIVITIES The main conference will take the form of a three-day single-track oral and poster presentation programme, 30th July to 1st August 2014, hosted at the Museo Nazionale Della Scienza E Della Tecnologia Leonardo Da Vinci in Milan (http://www.museoscienza.org). The conference programme will include five plenary lectures from leading international researchers in biomimetic and biohybrid systems, and the demonstrations of state-of-the-art living machine technologies. Agreed speakers are: Sarah Begreiter, University of Maryland (Microfabrication and robotics) Darwin Caldwell, Italian Institute of Technology (Legged locomotion) Andrew Schwartz, University of Minnesota, Pittsburgh (Neural control of prosthetics) Ricard Sole, Universitat Pompeu Fabra, Barcelona (Self-organization and synthetic biology) Srini Srinivasan, Queensland Brain Institute (Insect-inspired cognition and vision) There will also be a special session on biomimetics in design, including a talk by Franco Lodato, author of the book 'Bionics in Action.' The full conference will be preceded by up to two days of Satelite Events hosted by the Istituto Italiano di Technologia in Milan. SUBMITTING TO LIVING MACHINES 2014 We invite both full papers and extended abstracts in areas related to the conference themes. All contributions will be refereed and accepted papers will appear in the Living Machines 2014 proceedings published in the Springer-Verlag LNAI Series. Submissions should be made before the advertised deadline via the Springer submission site: http://senldogo0039.springer-sbm.com/ocs/en/home/LM2014 Full papers (up to 12 pages) are invited from researchers at any stage in their career but should present significant findings and advances in biomimetic or biohybid research; more preliminary work would be better suited to extended abstract submission (3 pages). Submitted papers should be prepared using the Springer proceedings format and instructions http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0 Full papers will be accepted for either oral presentation (single track) or poster presentation. Extended abstracts will be accepted for poster presentation only. Authors of the best full papers will be invited to submitted extended versions of their paper for publication in a special issue of Bioinspiration and Biomimetics. Satellite events Active researchers in biomimetic and biohybrid systems are invited to propose topics for 1-day or 2-day tutorials, symposia or workshops on related themes to be held 28-29th July at Italian Institute of Technology in Milan. Events can be scheduled on either the 28th or 29th or across both days. Attendance at satellite events will attract a small fee intended to cover the costs of the meeting. There is a lot of flexibility about the content, organisation, and budgeting for these events. Please contact us if you are interested in organising a satellite event! EXPECTED DEADLINES March 14th, 2014 Paper submission deadline April 29th, 2014 Notification of acceptance May 20th, 2014 Camera ready copy July 29-August 2nd 2014 Conference SPONSORSHIP Living Machines 2014 is sponsored by the Convergent Science Network (CSN) for Biomimetics and Neurotechnology. CSN is an EU FP7 Future Emerging Technologies Co-ordination Activity that also organises many highly successful workshop series: the Barcelona Summer School on Brain, Technology and Cognition, the Capo Caccia Neuromorphic Cognitive Engineering Workshop, the School on Neuro-techniques, the Okinawa School of Computational Neuroscience and the Telluride workshop of Cognitive Neuromorphic Engineering (see http://csnetwork.eu/activities for details) The 2014 Living Machines conference will also be hosted and sponsored by the Istituto Italiano di Technologia (http://www.iit.it). Call for Sponsors. Other organisations wishing to sponsor the conference in any way and gain the corresponding benefits by promoting themselves and their products to through conference publications, the conference web-site, and conference publicity are encouraged to contact the conference organisers to discuss the terms of sponsorship and necessary arrangements. We offer a number of attractive and good-value packages to potential sponsors. ABOUT THE VENUE Living Machines 2014 continues our practice of hosting our annual meeting in an inspirational venue related to the conference themes. The scientific and technological genius Leonardo da Vinci drew much of his inspiration from biology and invented many biomimetic artefacts. We are therefore delighted that this year's conference will be hosted at the Da Vinci museum of Science and Technology in Milan, one of the largest technology museums in Europe and host to a collection of working machines that realise many of Da Vinci's ideas. We look forward to seeing you in Milan. Organising Committee: Tony Prescott, University of Sheffield (Co-chair) Paul Verschure, Universitat Pompeu Fabra (Co-chair) Armin Duff, Universitat Pompeu Fabra (Program Chair) Giorgio Metta, Instituto Italiano di Technologia (Local Organizer) Barbara Mazzolai, Instituto Italiano di Technologia (Local Organiser) Anna Mura, Universitat Pompeu Fabra (Communications) Nathan Lepora, University of Bristol (Communications) Program Committee: Anders Lyhne Christensen Andy Adamatzky Andy Phillipides Arianna Menciassi Auke Ijspeert Barry Trimmer Ben Mitchinson Benoit Girard Cecilia Laschi Charles Fox Chrisantha Fernando Christophe Grand Danilo de Rossi Darwin Caldwell Dieter Braun Emre Neftci Enrico Pagello Eris Chinaletto Ferdinando Rodrigues y Baena Frank Grasso Fred Claeyssens Frederic Boyer Frederico Carpi Giacomo Indiveri Gregory Chirikjian Hillel Chiel Holger Krapp Holk Cruse Husosheng Hu Jess Krichmar Jira Okada John Hallam Jon Timmis Jonathan Rossiter Jose Halloy Joseph Ayers Julian Vincent Keisuke Morisima Lucia Beccai Marco Dorigo Mark Cutkosky Martin Pearson Mat Evans Mehdi Khamassi Michele Giogliano Nathan Lepora Noah Cowan Pablo Varona Paul Graham Paul Verschure Reiko Tanaka Robert Allen Roberto Cingolani Roderich Gross Roger Quinn Sean Anderson Serge Kernbach Simon Garnier Stephane Doncieux Stuart Wilson Thomas Schmickl Tim Pearce Tony Pipe Tony Prescott Volker Durr Wolfgang Eberle Yiannis Demiris Yoseph Bar-Cohen From birgit.ahrens at bcf.uni-freiburg.de Fri Mar 7 08:26:56 2014 From: birgit.ahrens at bcf.uni-freiburg.de (Birgit Ahrens) Date: Fri, 7 Mar 2014 14:26:56 +0100 Subject: Connectionists: Open Postdoc positions at Bernstein Center Freiburg, Germany Message-ID: <00b401cf3a08$e9b18b70$bd14a250$@bcf.uni-freiburg.de> Dear Computational Neuroscience Community, Please find below our job posting for Postdoc positions in the labs of Prof. Stefan Rotter, Prof. Ulrich Egert and Dr. Robert Schmidt / Dr. Arvind Kumar at the Bernstein Center Freiburg (BCF) , Germany. Best regards Birgit Ahrens PostDoc position on computational models of dopamine neuromodulation One PostDoc position is available from the Cluster of Excellence BrainLinks-BrainTools in Freiburg (Germany) in the research groups of Robert Schmidt and Arvind Kumar located at the Bernstein Center Freiburg. The project is focused on studying the effect of dopamine on the basal ganglia network dynamics and on motor output. In particular, simulations of basal ganglia circuits will be used to characterize the functional role of dopamine through the complex modulation of membrane and synaptic properties of striatal cells. The results of this project are expected to contribute to develop new treatment strategies for akinesia, rigidity, bradykinesia and levodopa-induced dyskinesia in Parkinson's Disease. The ideal candidate has a strong background in computational neuroscience demonstrated by peer-reviewed publications. Advanced programming skills (e.g. Matlab or Python), high motivation and a vivid interest in neuroscientific research are mandatory. Experience in the analysis of neurophysiological data and computational modelling is expected. The position is initially for one year (100% TV-L E13) and can be extended to a total of 30 months after successful internal evaluation of the project. Please send a single PDF containing your CV, and a scientific research statement (max. 2 pages) to dopamine at brainlinks-braintools.uni-freiburg.de. In addition, please arrange 2-3 referees to send their recommendation letters directly to the same e-mail address. Application deadline is the 31st March, 2014 and the position starts latest October 2014. Further details on: www.bcf.uni-freiburg.de/jobs Postdoc Position on Structure and Dynamics of Cortical Networks in the Computational Neuroscience lab of Prof. Stefan Rotter Our goal is to understand the interplay between network topology and spiking activity dynamics in the neocortex and other parts of the mammalian brain, and to explore the possibilities and constraints of dynamical brain function. Our main tools are mathematical/numerical network modeling and statistical data analysis, often used side by side within the framework of stochastic point processes and statistical graph theory. In collaboration with physiologists and anatomists, we seek to develop new perspectives for the model-based analysis and interpretation of neuronal signals. We are a young group of researchers from mathematics, physics, computer science and biology and invite applications to join the lab for a 2-3 year PostDoc project, and to enter the PostDoc program in Computational Neuroscience at the Bernstein Center Freiburg. Please apply online via our online application form and indicate "Rotter" as preferred project. Applications are accepted until the position is filled. Further details on: www.bcf.uni-freiburg.de/jobs Postdoc position in Non-Clinical Epilepsy Research in the Biomicrotechnology lab of Prof. Ulrich Egert We are currently offering a Postdoc position (2 years) in the Laboratory for Biomicrotechnology (http://www.bcf.uni-freiburg.de/people/details/egert) at the University of Freiburg. The project investigates mechanisms underlying mesiotemporal lobe epilepsy from the perspective of the dysfunction of interaction between subnetworks in the hippocampal formation (Froriep et al. 2012, Epilepsia). We aim to use targeted stimulation to reduce the circuit's susceptibility for seizures. It is essential that you have a background in neuroscience, ideally in experimental neurophysiology in vivo as well as a PhD degree. You should further be competent in data analysis and have an affinity for the network perspective. The project is part of the Cluster of Excellence "BrainLinks-BrainTools'' (www.brainlinks.uni-freiburg.de ) together with the Bernstein Center Freiburg (www.bcf.uni-freiburg.de ) and will combine neurophysiology, computational neuroscience and neurotechnology. Please apply online via our online application form and indicate "Egert" as preferred project. Applications are accepted until the position is filled. Further details on: www.bcf.uni-freiburg.de/jobs Postdoc position in Neurotechnology & Computational Neuroscience in the lab of Prof. Stefan Rotter We are looking for a postdoctoral researcher to join an international team of scientists and engineers in the NeuroSeeker project (see http://www.neuroseeker.eu/). The goal of the project is to develop and apply new methods and software to improve the yield of novel high-resolution probes for recording activity from many neurons simultaneously. Candidates should hold a PhD in physics, applied mathematics, computer science or biology, with proven experience in software engineering and user-oriented application programming (C++ and/or Python). Specific knowledge and scientific publications in the fields of "statistical analysis of neuronal data" and/or "numerical methods and data structures in the neurosciences" are a requirement. Funding is already available, starting date is negotiable. Please apply online via our online application form and indicate "Rotter" as preferred project. Applications are accepted until the position is filled. Further details on: www.bcf.uni-freiburg.de/jobs -- Dr. Birgit Ahrens -- Coordinator for the Teaching & Training Programs Bernstein Center Freiburg Albert-Ludwig University of Freiburg Hansastr. 9a D - 79104 Freiburg Germany Phone: +49 (0) 761 203-9575 Fax: +49 (0) 761 203-9559 Email: birgit.ahrens at bcf.uni-freiburg.de Web: www.bcf.uni-freiburg.de -------------- next part -------------- An HTML attachment was scrubbed... URL: From danko.nikolic at googlemail.com Fri Mar 7 11:22:05 2014 From: danko.nikolic at googlemail.com (Danko Nikolic) Date: Fri, 07 Mar 2014 17:22:05 +0100 Subject: Connectionists: how the brain works? Message-ID: <5319F22D.80609@gmail.com> I believe that the readers of Connectionists list my be interested in the manuscript available on arXiv (1402.5332) proposing the principles by which adaptive systems create intelligent behavior. It is a theoretical paper that has been recently submitted to a journal, and the editors agreed to post it on arXiv. A nice context for this manuscript is, I think, the recent discussion on Connectionists list on "how the brain works?", -- including the comparison to how the radio works, arguments that neuroscience has not reached the maturity of 19th century physics, that the development should be an essential component, etc. I assess that anyone who enjoyed following that discussion, like I did, would be interested also in what the proposed theory has to say. The theory addresses those problems by placing the question of brain workings one level more abstract than it is usually discussed: It proposes a general set of properties that adaptive systems need to have to exhibit intelligent behavior (nevertheless, concrete examples are given from biology and technology). Finally, the theory proposes what is, in principle, missing in the current approaches in order to account for the higher, biological-like levels of adaptive behavior. For those who are interested, I recommend using the link on my website: http://www.danko-nikolic.com/practopoiesis/ because there I provided, in addition, a simplified introduction into some of the main conclusions derived from the theory. I would very much like to know what people think. Comments will be appreciated. With warm greetings from Germany, Danko Nikolic -- Danko Nikolic, Ph.D. Web: http://www.danko-nikolic.com Mail address 1: Department of Neurophysiology Max Planck Institut for Brain Research Deutschordenstr. 46 60528 Frankfurt am Main GERMANY Mail address 2: Frankfurt Institute for Advanced Studies Wolfgang Goethe University Ruth-Moufang-Str. 1 60433 Frankfurt am Main GERMANY ---------------------------- Office: (..49-69) 96769-736 Lab: (..49-69) 96769-209 Fax: (..49-69) 96769-327 danko.nikolic at gmail.com ---------------------------- From poirazi at imbb.forth.gr Mon Mar 10 06:02:50 2014 From: poirazi at imbb.forth.gr (Yiota Poirazi) Date: Mon, 10 Mar 2014 12:02:50 +0200 Subject: Connectionists: DENDRITES 2014, July 1-4, Crete: DEADLINE EXTENSION & REGISTRATION WAIVERS Message-ID: <531D8DCA.9010203@imbb.forth.gr> Apologies for cross-posting! Please distribute to interested parties. many thanks! Yiota Poirazi ------------------------------------------------------- DENDRITES 2014 4^th NAMASEN Training Workshop Heraklion, Crete, Greece. July 1-4, 2014 http://dendrites2014.gr/ info at dendrites2014.gr * * * * * ABSTRACT SUBMISSION EXTENDED * * * * * Abstract submission for poster and oral presentation has been extended to *March 31st, 2014.* * * * * * REGISTRATION WAIVERS FOR STUDENTS/POSTDOCS* * * * * We are happy to announce the availability of registration waivers for the entire meeting. Waivers are provided by IBRO PERC funding for Symposia, Workshops & Meetings. Students and postdocs presenting an abstract are eligible. To request a waiver, please send an email to info at dendrites2014.org including information about your submitted abstract. Travel funds might also become available and will be announced on the web site. ---------------------------------------- Dendrites 2014 aims to bring together scientists from around the world to present their research on dendrites, ranging from the molecular to the anatomical and biophysical levels. The Workshop will take place in the beautiful island of Crete, just after the AREADNE meeting (areadne.org) which takes place in the nearby island of Santorini. Note that Santorini is just a 2 hour boat ride from Crete. One could combine two meetings in a single trip to Greece! FORMAT AND SPEAKERS The meeting consists of the Main Event (July 1-3) and the Soft Skills Day (July 4). Invited speakers for the Main Event include: * Susumu Tonegawa (Nobel Laureate), * Angus Silver, * Tiago Branco, * Alcino J. Silva, * Michael H?usser, * Julietta U. Frey, * Stefan Remy, and * Kristen Harris. For the Soft Skills Day, Kerri Smith, Nature podcast editor, is going to present on communication and dissemination of scientific results. Alcino J. Silva (UCLA) will present his recent work on developing tools for integrating and planning research in Neuroscience. There will be a hands on session on tools used to model neural cells/networks and a talk about the advantages of publishing under the Creative Commons License. CALL FOR ABSTRACTS Submission of abstracts for poster and oral presentations are due by *31 March 2014*. We welcome both experimental and theoretical contributions addressing novel findings related to dendrites. For details, please see http://dendrites2014.gr/call/. Electronic abstract submission is hosted on the Frontiers platform, where extended abstracts will be published as an online, open access special issue (in Frontiers in Cellular Neuroscience, Impact factor 4.5). One of the authors has to register for the main event as a presenting author. In case an abstract is not accepted for presentation, the registration fee will be refunded. FOR FURTHER INFORMATION Please see the conference web site (http://dendrites2014.gr/), subscribe to our twitter (https://twitter.com/dendrites2014) or RSS feeds (http://dendrites2014.gr/rss_feed/news.xml), or send an email to info at dendrites2014.org . -- Panayiota Poirazi, Ph.D. Research Director Institute of Molecular Biology and Biotechnology (IMBB) Foundation of Research and Technology-Hellas (FORTH) Vassilika Vouton, P.O.Box 1385 GR 711 10 Heraklion, Crete, GREECE Tel: +30 2810 391139 Fax: +30 2810 391101 ?mail:poirazi at imbb.forth.gr http://www.dendrites.gr -------------- next part -------------- An HTML attachment was scrubbed... URL: From peter.ljunglof at heatherleaf.se Mon Mar 10 09:16:39 2014 From: peter.ljunglof at heatherleaf.se (=?iso-8859-1?Q?peter_ljungl=F6f?=) Date: Mon, 10 Mar 2014 14:16:39 +0100 Subject: Connectionists: CALL FOR PARTICIPATION: EACL 2014 Tutorial on Recent Advances in Dependency Parsing Message-ID: <3511C8EF-8671-45F6-84D5-480B02E344D9@heatherleaf.se> CALL FOR PARTICIPATION EACL 2014 Tutorial: Recent Advances in Dependency Parsing Ryan McDonald, Joakim Nivre Gothenburg, Sweden, Sunday 27 April 2014 http://eacl2014.org/tutorial-dependency-parsing Syntactic parsing is a fundamental problem in natural language processing which has been tackled using a wide variety of approaches. In recent years, there has been a surge of interest in parsers that make use of dependency structures, which offer a simple and transparent encoding of predicate-argument structure and can be derived accurately and efficiency using parsers trained on annotated corpora. Thanks to their simplicity, transparency and efficiency, dependency parsers are in widespread use for applications such as information extraction, question answering, machine translation, language modeling, semantic role labeling, and textual entailment. This tutorial will focus on advances in dependency parsing that are not covered in textbooks or previous tutorials, which means roughly work from 2008 and onwards. However, in order to make the material accessible to participants without a background in dependency parsing, we will spend roughly the first quarter of the tutorial going over basic concepts and techniques in the field, including the theoretical foundations of dependency grammar and basic definitions of representations, tasks, and evaluation metrics. After reviewing the basic concepts, we will introduce the two dominant paradigms in early work on data-driven dependency parsing -- global, exhaustive, graph-based parsing and local, greedy, transition-based parsing -- and review the contrastive error analysis presented in McDonald and Nivre (2007), which highlighted the strengths and weaknesses of the two models and set the challenge to improve both graph-based and transition-based methods. This provides a basis for understanding many of the later developments covered in the tutorial. The rest of the tutorial will be divided into two main parts, covering advances in graph-based parsing and related approaches, on the one hand, and advances based on transition-based parsing, on the other. We will finish off with a synthesizing conclusion and outlook for the future. Research on graph-based dependency parsing in recent years has to a large extent been driven by the wish to make efficient use of higher-order models, thereby overcoming the limitations of strictly local feature representations found in early models. As a consequence, there has been developments towards specialized exact inference and approximate inference methods, the latter especially for non-projective parsing. In addition, there has been work on trying to find exact dynamic programming solutions for restricted subsets of non-projective structures, often referred to as mildly non-projective dependency trees. Recent work on transition-based dependency parsing has focused on two lines of research, often in combination. The first line has been concerned with improving the search techniques through the use of beam search, dynamic programming, and easy-first inference, thereby overcoming the limitations of greedy left-to-right search. The second line has been to improve the learning methods by moving to global structured learning and or imitation learning with exploration, thereby countering the negative effects of local classifier learning. In addition, there has been work on joint morphological and syntactic analysis. From jesus.m.cortes at gmail.com Mon Mar 10 08:20:33 2014 From: jesus.m.cortes at gmail.com (Jesus Cortes) Date: Mon, 10 Mar 2014 13:20:33 +0100 Subject: Connectionists: Ikerbasque Research Fellow ("Tenure"). Methods in Neuroimaging, Bilbao (Spain) Message-ID: Dear researcher, One position for "Ikerbasque Research Fellow" will be host by the new emerging group of Computational Neuroimaging in Biocruces. http://www.biocruces.com/l1003 More information about the position, "Ikerbasque Research Fellow" at http://www.ikerbasque.net/fellows More information about Biocruces http://www.biocruces.com/ Interested researchers must contact me before March 15th, 2014. Feel free in contacting me for any further question or comment. Provide a copy of an updated CV remarking best 5 publications. Prof. Jesus M Cortes Ikerbasque Research Professor Head of the Computational Neuroimaging Lab Biocruces Health Research Institute -------------- next part -------------- An HTML attachment was scrubbed... URL: From peter.ljunglof at heatherleaf.se Tue Mar 11 03:20:16 2014 From: peter.ljunglof at heatherleaf.se (=?iso-8859-1?Q?peter_ljungl=F6f?=) Date: Tue, 11 Mar 2014 08:20:16 +0100 Subject: Connectionists: [Corpora-List] EACL 2014 Tutorial on Computational modelling of metaphor Message-ID: <2E3333F1-B861-470B-BCE5-E2ED3D3DC9F4@heatherleaf.se> CALL FOR PARTICIPATION EACL 2014 Tutorial on Computational modelling of metaphor Gothenburg, Sweden, 26 April http://eacl2014.org/tutorial-metaphor Instructors: Ekaterina Shutova and Tony Veale TUTORIAL DESCRIPTION Metaphor processing is a rapidly growing area in NLP. Characteristic to all areas of human activity (from the ordinary to the poetic or the scientific) and, thus, to all types of discourse, metaphor poses an important problem for NLP systems. Its ubiquity in language has been established in a number of corpus studies and the role it plays in human reasoning has been confirmed in psychological experiments. This makes metaphor an important research area for computational and cognitive linguistics, and its automatic identification and interpretation indispensable for any semantics-oriented NLP application. Computational work on metaphor in NLP and AI ignited in the 1970s and gained momentum in the 1980s, providing a wealth of ideas on the form, structure and mechanisms of the phenomenon. The last decade has witnessed a technological leap in natural language computation, as manually crafted rules have gradually given way to more robust corpus-based statistical methods. This is also the case for metaphor research. In the recent years, the problem of metaphor modeling has been steadily gaining interest within the NLP community, with a growing number of approaches exploiting statistical techniques. Compared to more traditional approaches based on hand-coded resources, these more recent methods boast of a wider coverage, as well as greater efficiency and robustness. However, even the statistical metaphor processing approaches largely focus on a limited domain or a subset of conceptual phenomena. At the same time, recent work on computational lexical semantics and lexical acquisition techniques, as well as a wide range of NLP methods applying machine learning to open-domain semantic tasks, opens many new avenues for creation of large-scale robust tools for the recognition and interpretation of metaphor. Despite a growing recognition of the importance of metaphor to the semantic and affective processing of language, and despite the availability of new NLP tools that enable us to take metaphor processing to the next level, educational initiatives for introducing the NLP community to this fascinating area of research have been relatively few in number. Our proposed tutorial thus addresses this gap, by aiming to: introduce a CL audience to the main linguistic, conceptual and cognitive properties of metaphor; cover the history of metaphor modelling and the state-of-the-art approaches to metaphor identification and interpretation analyse the trends in computational metaphor research and compare different types of approaches, aiming to identify the most promising system features and techniques in metaphor modelling discuss potential applications of metaphor processing in wider NLP relate the problem of metaphor modelling to that of other types of figurative language The tutorial is targeted both at participants who are new to the field and need a comprehensive overview of metaphor processing techniques and applications, as well as at experienced scientists who want to stay up to date on the recent advances in metaphor research. TUTORIAL OUTLINE Introduction: Linguistic, cognitive and cultural properties of metaphor Linguistic metaphor Conceptual metaphor Metaphorical inference Extended metaphor / metaphor in discourse Conventional and novel metaphor Metaphor in corpora and lexical resources Computational approaches to metaphor identification Knowledge-based methods Lexical resource-based methods Metaphor and selectional preferences Metaphor and abstractness Metaphor and cultural stereotypes Word similarity and association-based methods Supervised learning for metaphor identification Weakly-supervised and unsupervised methods Computational approaches to metaphor interpretation Knowledge-based methods Metaphor interpretation by explanation (SlipNet) Metaphor interpretation as paraphrasing (supervised and unsupervised) Challenges in metaphor generation Applications of metaphor processing systems Metaphor in machine translation Metaphor in opinion mining Metaphor in information retrieval Metaphor in educational applications Metaphor in social science Metaphor in psychology Metaphor and other types of figurative language Metaphor and blending Metaphor and simile Metaphor and analogy Metaphor and irony We look forward to seeing you at the tutorial! Katia and Tony -------------- next part -------------- An HTML attachment was scrubbed... URL: From ASJagath at ntu.edu.sg Mon Mar 10 23:20:28 2014 From: ASJagath at ntu.edu.sg (Jagath C Rajapakse (Prof)) Date: Tue, 11 Mar 2014 03:20:28 +0000 Subject: Connectionists: Research positions/PhD student positions in brain imaging: MRI, DTI, fMRI and PET Message-ID: Research positions and PhD student positions in multimodal brain imaging: MRI, DTI, fMRI and PET Research positions (PhD/MSc) and PhD student positions in image analysis for multimodal brain imaging with MRI, DTI, fMRI and PET are available at the Nanyang Technological University (NTU). http://www.ntu.edu.sg/Pages/home.aspx This is a collaborative project between researchers from the School of Computer Engineering and the LKC Medical School to study brain function and disorders by combining different image modalities. Experience in brain image analysis and programming skills (e.g. Matlab) would be added advantages. Please send your CV and a brief research statement to Professor Jagath Rajapakse (asjagath at ntu.edu.sg). ________________________________ CONFIDENTIALITY:This email is intended solely for the person(s) named and may be confidential and/or privileged.If you are not the intended recipient,please delete it,notify us and do not copy,use,or disclose its contents. Towards a sustainable earth:Print only when necessary.Thank you. -------------- next part -------------- An HTML attachment was scrubbed... URL: From ecai2014 at guarant.cz Tue Mar 11 11:40:03 2014 From: ecai2014 at guarant.cz (ecai2014 at guarant.cz) Date: Tue, 11 Mar 2014 16:40:03 +0100 Subject: Connectionists: =?utf-8?q?ECAI_2014_-_REGISTRATION_OPEN?= Message-ID: <20140311154003.D971317418A@gds25d.active24.cz> ECAI 2014 - REGISTRATION OPEN Image: http://dev.topinfo.cz/guarant.mailing/img/_/mailing/3372/header.jpg REGISTRATION OPEN For registration to ECAI 2014 please use the on-line registration system availableon?www.ecai2014.org/registration/ You may register for: ????Main conference (August 20???22) ????Workshops (August 18???19) ????Tutorials (August 18???19) ????PAIS (August 20???21) ????STAIRS (August 18???19) ????RuleML (August 18???20) ????Angry Birds Competition (August 18???22) and also Conference dinner (August 21) Detailed information can be found on www.ecai2014.org We look forward to meeting you in Prague. Conference Secretariat GUARANT International Na Pankr??ci 17 140 21 Prague 4 Tel: +420 284 001 444, Fax: +420 284 001 448 E-mail: ecai2014 at guarant.cz Web: www.ecai2014.org This email is not intended to be spam or to go to anyone who wishes not to receive it. If you do notwish to receive this letter and wish to remove your email address from our database pleasereply to this message with ???Unsubscribe??? in the subject line. -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: header.jpg Type: image/jpeg Size: 70793 bytes Desc: not available URL: From terry at salk.edu Tue Mar 11 12:40:28 2014 From: terry at salk.edu (Terry Sejnowski) Date: Tue, 11 Mar 2014 09:40:28 -0700 Subject: Connectionists: NEURAL COMPUTATION - April, 2014 In-Reply-To: Message-ID: Neural Computation - Contents -- Volume 26, Number 4 - April 1, 2014 Available online for download now: http://www.mitpressjournals.org/toc/neco/26/4 ----- Article Parametric Inference in the Large Data Limit Using Maximally Informative Models Justin Block Kinney, Gurinder Singh Atwal Letters Input Statistics and Hebbian Crosstalk Effects Anca R. Radulescu Learning Nonlinear Regularities in Natural Images by Modeling the Outer Product of Image Intensities Peng Qi, Xiaolin Hu Dissociable Forms of Repetition Priming: A Computational Model Kirill Makukhin, Scott Bolland Refined Rademacher Chaos Complexity Bounds With Applications to the Multi-Kernel Learning Problem Yunwen Lei, Lixin Ding Large Margin Low Rank Tensor Analysis Guoqiang Zhong, Mohamed Cheriet Large-scale Linear RankSVM Ching-Pei Lee, Chih-Jen Lin ------------ ON-LINE -- http://www.mitpressjournals.org/neuralcomp SUBSCRIPTIONS - 2014 - VOLUME 26 - 12 ISSUES USA Others Electronic Only Student/Retired $70 $193 $65 Individual $124 $187 $115 Institution $1,035 $1,098 $926 Canada: Add 5% GST MIT Press Journals, 238 Main Street, Suite 500, Cambridge, MA 02142-9902 Tel: (617) 253-2889 FAX: (617) 577-1545 journals-orders at mit.edu ------------ From giacomo.cabri at unimore.it Mon Mar 10 08:02:10 2014 From: giacomo.cabri at unimore.it (Giacomo Cabri) Date: Mon, 10 Mar 2014 13:02:10 +0100 Subject: Connectionists: SASO 2014 - Call for Workshops and Tutorials Message-ID: <531DA9C2.3070509@unimore.it> SASO 2014 - Call for Workshops and Tutorials 8th IEEE International Conference on Self-Adaptive and Self-Organizing Systems London, UK; 8-12 September 2014 http://www.saso-conference.org ================================ ********************* 1. Call for workshops ********************* The SASO 2014 Steering Committee invites proposals for the Workshop Program to be held along with the technical conference. SASO 2014 workshops will provide a meeting for presenting novel ideas in a less formal and possibly more focused way than the conference itself. Its aim is to stimulate and facilitate active exchange, interaction, and comparison of approaches, methods, and ideas related to specific topics, both theoretical and applied, in the general area of Self-Adaptive and Self-Organizing Systems. To motivate the discussion and participation of all the workshop attendants, we encourage organizers to get away of the typical "mini-conference" format of a workshop, and include more discussion sessions, panels, etc. Members from all areas of the SASO community are invited to submit workshop proposals for review. Workshops on global challenges, applications or on new and emerging topics are particularly encouraged. Workshops can vary in length, but most will be one full day in duration. Optionally, if desired by the organizers, workshop proceedings can be published through IEEE. Attendance of workshops will be included in the registration fee for the main SASO conference. Important Dates =============== Proposal Submission Deadline: April 11, 2014 Acceptance Notification: April 18, 2014 CFP Submission Deadline: April 25, 2014 Paper Submission Deadline: July 11, 2014 11 Paper Acceptance Notification: August 1, 2014 Early Registration Deadline: TBD Camera-Ready Papers due: August 13, 2014 Workshop notes submission to WS chairs: August 18, 2014 Workshops dates: September 8 & 12, 2014 SASO 2014 Workshops - Requirements for Submission ================================================= Proposals for workshops should be separated in two parts. The first part should be organized as preliminary call for papers or call for participation, depending on the intended format of the workshop with a maximum of two pages and contain the following information: - Title of the workshop. - A brief technical description of the workshop, specifying the workshop goals, the technical issues that it will address, and the relevance of the workshop to the main conference. - Description of paper review process (if any) and acceptance standards in order to keep the workshop high in quality. Note that papers must be in the same format as the conference proceedings and may not be more than 6 pages in length. - The names, affiliations, postal addresses, phone numbers, and email addresses of the proposed workshop organizing committee. This committee should consist of three or four people knowledgeable about the technical issues to be addressed. The organizing committee should include individuals from multiple institutions. - The primary email address for contacting the organizing committee. - Expected duration of the workshop (half or full day). - A brief description of the workshop format. - List of potential program committee members (if applicable), including their title and affiliations. - List of potential invited speakers, panelists, or disputants (if applicable). The second part with a maximum of three pages should contain additional information not suitable for a Call for Papers, including: - A discussion of why and to whom the workshop is of interest. - A list of related workshops held within the last three years, if any, and their relation to the proposed workshop. - Information about previous offerings of the proposed workshop: when and where it has been offered in the past, organizers names and affiliations, number of submissions, acceptances and registered attendees. - A description of the qualifications of the individual committee members with respect to organizing a SASO workshop, including a list of workshops previously arranged by any members of the proposed organizing committee, if any. - A list of places (distribution lists, web sites, journals, etc.) where the workshop is planned to be advertised. All proposals should be submitted in plain ASCII text or PDF format by electronic mail to the SASO 2014 Workshops Chairs. The selection of the workshops to be included in the final SASO 2014 Workshop program will be based upon multiple factors, including: - the scientific/technical interest of the topics, - the quality of the proposal, - complementarity with the conference topics, - balance and distinctness of workshop topics, and - the capacity of the conference workshop program. Note that authors of proposals addressing similar and/or overlapping content areas and/or audiences may be requested to merge their proposals. Responsibilities of SASO 2014 and Workshop Organizers ===================================================== For all accepted proposals, SASO 2014 will be responsible for: - Providing publicity for the workshop series as a whole. - Providing logistical support and a meeting place for the workshop. - Together with the organizers, determining the workshop date and time. - Liaising and coordinating between the workshop chairs and the finance chair, publicity chair, registration chair, and web chair for SASO. - Arranging for publication of proceedings. Workshop organizers will be responsible for the following: - Setting up a web site for the workshop. - Advertising the workshop (and the main SASO conference), and issuing a call for papers and a call for participation. - Collecting and evaluating submissions, notifying authors of acceptance or rejection on a timely basis, and ensuring a transparent and fair selection process. All workshop organizers commit themselves to adopt the deadlines set by the committee. - Making the pdf of the whole workshop notes available to the workshop chair, as well as a list of audio-visual requirements and any special room requirements. - Writing a 1-page organizers introduction for your proceedings. - Ensuring that the workshop organizers and the participants register for the workshop and/or the main conference (at least one author must register for the paper to appear in the proceedings). SASO reserves the right to cancel any workshop if the above responsibilities are not fulfilled, or if too few attendees register for the workshop to support its running costs. Submissions and Inquiries ========================= Please send proposals (as a PDF document) and inquiries to the SASO 2014 workshop chairs: Jan-Philipp Stegh?fer (University of Augsburg, Germany) steghoefer at informatik.uni-augsburg.de Didac Busquets (Imperial College London, UK) didac.busquets at imperial.ac.uk ************************** 2. Call for tutorials ************************** Important Dates =============== The exact deadlines are at 11:59 PM CET (Central European Time). Proposal Submission Deadline: April 18, 2014 Presenter Notification: April 25, 2014 Extended Abstract and Presentation Handouts due: August 1, 2014 Tutorial presentation: September 8 & 12, 2014 Call for Tutorials ================== SASO 2014 will host a tutorial program, to be held in Imperial College London, UK, in the week of September 8-12, 2014. For detailed information about SASO 2014 please visit the conference website. Goal and Scope ============== The goal of the tutorial program of SASO 2014 is to provide an instructional offer to scholars, practitioners and students attending the conference, on a range of topics related to self-adaptive and self-organizing systems. We plan to accommodate either half-day tutorials (approx. 3.0 hours, plus one 30-minutes break) and full-day tutorials (approx 6.0 hours, plus two 30-minutes breaks, and a lunch break). The topic of a tutorial may range from practical techniques and technologies, to methodologies, guidelines over standards, to theoretical aspects related to self-adaptive and self-organizing systems (software, networks or services). The topic areas that fall into the general scope of SASO, as well as the focus of this year's conference, are listed in the Call for Papers that is available on the conference web site. Please note that no marketing or product specific tutorials will be accepted. Tutorial levels may be introductory, intermediate, or advanced. Topics that can capture the interest of a broad audience of scholars, practitioners or students are preferred. Review Process ============== Each tutorial proposal will be evaluated by the SASO organizing committee according to relevance and to SASO and its community, attractiveness and novelty of the topic, consistency with the focus of the conference, general fit within the overall tutorial program, and previous teaching experience of the proposers. Submission Guidelines ===================== Tutorial proposals must not be longer than three pages, in the same format of the SASO research papers, that is, compliant with the IEEE Computer Society Press proceedings style. Please make sure the submission includes the following elements: - title of the tutorial; - preferred duration (half-day or full-day); - intended level (introductory, intermediate, or advanced) and prerequisites - contact information for all presenters, including full name, affiliation, email address, full postal address, phone and fax number, URL of personal homepage; - short bio of all presenters including prior teaching and tutorial experiences; - description of the material covered by the tutorial, not exceeding two pages (approx. 1500 words): must include a proposed structure of the content to be presented; - identification of the target audience (e.g., researchers, teachers, practitioners, students); - references of publications (books, papers etc.) the tutorial builds on; - indication of whether the submission of a tutorial paper (see below) is planned. All proposals should be submitted as a PDF document via email to the tutorial chair at the following address: a.artikis at gmail.com Accepted Proposals ================== Once notified that the tutorial has been accepted, tutorial proposers should prepare a two-page extended abstract - compliant with the IEEE Computer Society Press proceedings style - describing the content of the tutorial, in addition to the handout material that is going to be distributed to the participants to their tutorial. Both the extended abstract and the handout material must be ready and submitted to the Tutorial Chair by the deadline noted above. Submissions and Inquiries ========================= Please send proposals and inquiries to the SASO 2014 tutorial chair: Alexander Artikis -- |----------------------------------------------------| | Prof. Giacomo Cabri - Ph.D., Associate Professor | Dip. di Scienze Fisiche, Informatiche e Matematiche | Universita' di Modena e Reggio Emilia - Italia | e-mail giacomo.cabri at unimore.it | tel. +39-059-2058320 fax +39-059-2055216 |----------------------------------------------------| From giacomo.cabri at unimore.it Mon Mar 10 09:15:34 2014 From: giacomo.cabri at unimore.it (Giacomo Cabri) Date: Mon, 10 Mar 2014 14:15:34 +0100 Subject: Connectionists: CfP: The Eight IEEE International Conference on Self-Adaptive and Self-Organizing Systems, (SASO 2014) Message-ID: <531DBAF6.9030204@unimore.it> ************************************************************************************************************ CALL FOR PAPERS The Eight IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2014) Imperial College, London (UK); 8-12 September 2014 http://www.iis.ee.imperial.ac.uk/saso2014/ ************************************************************************************************************ Part of FAS* - Foundation and Applications of Self* Computing Conferences Collocated with: The International Conference on Cloud and Autonomic Computing (CAC 2014) The 14th IEEE Peer-to-Peer Computing Conference ------------------- Aims and Scope ------------------- The aim of the Self-Adaptive and Self-Organizing systems conference series (SASO) is to provide a forum for the foundations of a principled approach to engineering systems, networks and services based on self-adaptation and self-organization. The complexity of current and emerging networks, software and services, especially in dealing with dynamics in the environment and problem domain, has led the software engineering, distributed systems and management communities to look for inspiration in diverse fields (e.g., complex systems, control theory, artificial intelligence, sociology, and biology) to find new ways of designing and managing such computing systems. In this endeavor, self-organization and self-adaptation have emerged as two promising interrelated approaches. The eight edition of the SASO conference embraces the inter-disciplinarity and the scientific, empirical and application dimensions of self-* systems and welcomes novel results on both self-adaptive and self-organizing systems research. The topics of interest include, but are not limited to: - Self-* systems theory: theoretical frameworks and models; biologically- and socially-inspired paradigms; inter-operation of self-* mechanisms; - Self-* systems engineering: reusable mechanisms, design patterns, architectures, methodologies; software and middleware development frameworks and methods, platforms and toolkits; hardware; self-* materials; - Self-* system properties: robustness, resilience and stability; emergence; computational awareness and self-awareness; reflection; - Self-* cyber-physical and socio-technical systems: human factors and visualization; self-* social computers; crowdsourcing and collective awareness; - Applications and experiences of self-* systems: cyber security, transportation, computational sustainability, big data and creative commons, power systems. Contributions must present novel theoretical or experimental results; novel design patterns, mechanisms, system architectures, frameworks or tools; or practical approaches and experiences in building or deploying real-world systems and applications. Contributions contrasting different approaches for engineering a given family of systems, or demonstrating the applicability of a certain approach for different systems, are equally encouraged. Where relevant and appropriate, accepted papers will also be encouraged to submit accompanying papers for the Demo or Poster Sessions. -------------------- Important Dates -------------------- Abstract submission: May 2, 2014 Paper submission: May 09, 2014 Notification: June 21, 2014 Camera ready copy due: July 18, 2014 Early registration: August 22, 2014 Conference: September 8 - 12, 2014 ---------------------------- Submission Instructions ---------------------------- All submissions should be 10 pages and formatted according to the IEEE Computer Society Press proceedings style guide and submitted electronically in PDF format. Please register as authors and submit your papers using the SASO 2014 conference management system, which is located at: https://www.easychair.org/conferences/?conf=saso2014 The proceedings will be published by IEEE Computer Society Press, and made available as a part of the IEEE digital library. Note that a separate call for poster submissions has also been issued. -------------------- Review Criteria -------------------- Papers should present novel ideas in the cross-disciplinary research context described in this call, clearly motivated by problems from current practice or applied research. We expect both theoretical and empirical contributions to be clearly stated, substantiated by formal analysis, simulation, experimental evaluations, comparative studies, and so on. Appropriate reference must be made to related work. Because SASO is a cross-disciplinary conference, papers must be intelligible and relevant to researchers who are not members of the same specialized sub-field. Authors are also encouraged to submit papers describing applications. Application papers are expected to provide an indication of the real world relevance of the problem that is solved, including a description of the deployment domain, and some form of evaluation of performance, usability, or comparison to alternative approaches. Experience papers are also welcome but they must clearly state the insight into any aspect of design, implementation or management of self-* systems which is of benefit to practitioners and the SASO community ------------------- Program Chairs ------------------- Ada Diaconescu Telecom ParisTech, France Nagarajan Kandasamy Drexel University, USA Mirko Viroli University of Bologna, Italy --------------------- Contact Details --------------------- Please send any inquiries to: mailto:saso2014 at easychair.org -- |----------------------------------------------------| | Prof. Giacomo Cabri - Ph.D., Associate Professor | Dip. di Scienze Fisiche, Informatiche e Matematiche | Universita' di Modena e Reggio Emilia - Italia | e-mail giacomo.cabri at unimore.it | tel. +39-059-2058320 fax +39-059-2055216 |----------------------------------------------------| From zoltan.szabo.list at gmail.com Wed Mar 12 15:36:54 2014 From: zoltan.szabo.list at gmail.com (Zoltan Szabo) Date: Wed, 12 Mar 2014 19:36:54 +0000 Subject: Connectionists: ITE toolbox: new features, JMLR Message-ID: <5320B756.50907@gmail.com> (apologies for cross-posting) Dear Connectionists, The estimation of information theoretical quantities is of crucial importance in numerous problems of computational neuroscience. The redesigned ITE (Information Theoretical Estimators) toolbox, which obtained many new features and functionalities since its initial release, might be of your interest in this respect. The package has recently appeared in JMLR, a short summary of its current capabilities: --------------------------------------------------------------------------------- Information Theoretical Estimators Toolbox Zolt?n Szab?; JMLR 15(Jan):283-287, 2014. Link: http://jmlr.org/papers/v15/szabo14a.html Abstract We present ITE (information theoretical estimators) a free and open source, multi-platform, Matlab/Octave toolbox that is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities, and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. ITE also includes a prototype application in a central problem class of signal processing, independent subspace analysis and its extensions. --------------------------------------------------------------------------------- ITE is hosted on Bitbucket ("https://bitbucket.org/szzoli/ite/"); feel free to use it. Best, Zoltan -- Zoltan Szabo Gatsby Computational Neuroscience Unit University College London http://www.gatsby.ucl.ac.uk/~szabo/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From ray.subhasis at gmail.com Wed Mar 12 05:00:30 2014 From: ray.subhasis at gmail.com (Subhasis Ray) Date: Wed, 12 Mar 2014 14:30:30 +0530 Subject: Connectionists: Summer school at Bangalore, India: Computational Approaches to Learning and Memory Message-ID: [with apologies for cross-posting] Announcing a new course: Computational Approaches to Memory and Plasticity (CAMP @ Bangalore) . Venue: National Centre for Biological Sciences, Bangalore, India Dates: 28th June to 12th July, 2014 Organizer: Upinder Bhalla (NCBS, Bangalore, India) CAMP @ Bangalore is a 15-day summer school on the theory and simulation of learning, memory and plasticity in the brain. PhD students and post-docs from theoretical and/or experimental backgrounds (physics, math, neuroscience, engineering, etc.) are welcome. Familiarity with programming, dynamical systems, and/or computational neuroscience is highly desirable. Master's or Bachelor's degree students can apply if they have sufficient background. The course will start with remedial tutorials on neuroscience / math / programming and then work upwards from sub-cellular electrical and chemical signaling in neurons, onward to micro-circuits and networks, all with an emphasis on learning, memory and plasticity. Students worldwide are encouraged to apply online at www.ncbs.res.in/camp latest by 30th Mar, 2014. A poster for the school can be downloaded at the course website. Please circulate the information widely. Lecturers: Sumantra Chattarji (NCBS, Bangalore) Xiao-Jing Wang (New York University, New York) Suhita Nadkarni (Indian Institute of Science Education and Research, Pune) Michael Hausser (University College, London) Eve Marder (Brandeis University, Waltham/Boston) Rishikesh Narayanan (Indian Institute of Science, Bangalore) Arthur Wingfield (Brandeis University, Waltham/Boston) Claudia Clopath (Imperial College, London) Arvind Kumar (Bernstein Center, Freiburg) Stefano Fusi (Columbia University, New York) Raghav Rajan (Indian Institute of Science Education and Research, Pune) For more information, email camp2014 at ncbs.res.in Best regards, - Subhasis ( ??b?a???? ) ---- Subhasis Ray, Prof Upinder S Bhalla's lab, National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK, Bellary Road, Bangalore - 560065, India. Ph: 91-80-23666520. Best, - Subhasis ( ??b?a???? ) -------------- next part -------------- An HTML attachment was scrubbed... URL: From giacomo.cabri at unimore.it Mon Mar 10 13:00:03 2014 From: giacomo.cabri at unimore.it (Giacomo Cabri) Date: Mon, 10 Mar 2014 18:00:03 +0100 Subject: Connectionists: SASO 2014 - Call for Demos and Posters Message-ID: <531DEF93.90308@unimore.it> ===================================================================== SASO 2014 - CALL FOR DEMOS AND POSTERS 8th IEEE International Conference on Self-Adaptive and Self-Organizing Systems London, UK; 8-12 September 2014 http://www.saso-conference.org ===================================================================== ***************** 1. CALL FOR DEMOS ***************** ? IMPORTANT DATES: ? Deadline for demo submission: June 25, 2014 Notification of acceptance or rejection: July 25, 2014 Demo session date: September, 2014 ? CALL FOR DEMOS: ? The demonstration track at SASO 2014 aims at providing an opportunity to participants from academia and industry to present their latest applications and systems to fellow researchers and practitioners in the field. Submissions will be evaluated based on their overall self-* characteristics, originality and maturity. The committee will particularly consider system robustness, resilience and scaling abilities in addition to the self-* functions of the contributions. Interactivity of the demos will be considered a further asset. Demonstrations may target: * virtual systems, such as software applications; * physical systems, such as robots or sensor networks; * cyber-physical systems, combining the above; where physical systems might be presented either with real equipment, by simulation, or hybrid demos using both simulations and real platforms. We particularly solicit authors to highlight the utility and general applicability of their contributions, whether for the short, medium or long term. This call is open to the full range of conference topics, however we encourage authors to also consider socio-inspired and normative systems as focus issue. These comprise self-adaptive and self-organizing algorithms, platforms and coordination mechanisms which are influenced by the design or modeling of social and normative systems. For a detailed list of relevant topics, please refer to the SASO 2014 website or SASO 2014 CFP. ? SUBMISSION: ? Demo submissions must include: * a short paper (2 pages, conference format) describing the system and its self-* capabilities; if accepted, papers will be published in the official proceedings; * a URL of a website providing a self-explanatory video showing the system at work; and (optionally) allowing viewers to interact with the real system or with an emulator. Electronic submission: http://www.saso-conference.org At the conference, software applications will be presented on computers. For cyber-physical systems, if possible, authors are invited to bring their equipment (smart devices, sensors, actuators, robots, et cetera). Software simulations or video recordings can be accepted as an alternative. Additionally, authors must bring a poster summarizing their system and demo. ? EVALUATION AND AWARDS: ? Submitted demos will undergo a selection process based to equal parts on the quality of the short paper (novelty and impact, technical soundness and presentation) and the online demo system (design, degree of innovation, technical solution, applicability, clarity of the contribution and potential of reuse). At least one author of accepted demos is required to register to the conference and to do an on-site presentation and demonstration of the contributions to the evaluation committee, as well as the other conference attendees. The evaluation committee, consisting of the Demo Program Committee members attending the conference, will award a prize for the best demo in each system category, with cyber-physical systems classifying for both. ? ORGANIZATION: ? Please contact the chairs for any questions regarding the Demo session. The list of committee members comprising an international group of judges from academia and industry will be made available as soon as possible. ? CHAIRS: ? * Jean Botev, University of Luxembourg, LU; Email: jean.botev at uni.lu * Maite Lopez-Sanchez, University of Barcelona, ES; Email: maite_lopez at ub.edu ? PROGRAM COMMITTEE: ? * Tina Balke, University of Surrey, UK * Jose Luis Fernandez-Marquez, University of Geneva, CH * Kurt Geihs, University of Kassel, DE * Juan Antonio Rodri?guez-Aguilar, AI Research Institute (IIIA-CSIC), ES * Steffen Rothkugel, University of Luxembourg, LU * Ingo Scholtes ETH Zurich, CH * Jaime Sim?o Sichman, University of S?o Paulo, BR ******************* 2. CALL FOR POSTERS ******************* Important Dates (All deadlines are at 11:59 PM GMT) Deadline for submission: June 9, 2014 Notification of acceptance or rejection: June 30, 2014 Camera ready poster abstract due: July 18, 2014 Early registration deadline: August 22, 2014 Call for Posters ================ Overview The seventh SASO conference continues its tradition of offering poster sessions, a great opportunity for interactive presentation of emerging ideas, late-breaking results, experiences, and challenges on SASO topics. Poster sessions are informal and highly interactive, and allow authors and participants to engage in in-depth discussions about the presented work from which new collaborations, ideas, and solutions can emerge. Posters should cover the same key areas as Research Papers and should contain original cutting-edge ideas, as well as speculative/provocative ones. Proposals of new research directions and innovative interdisciplinary approaches are also welcome. Submissions in the following areas are particularly encouraged: Self-* systems theories, frameworks, models, and paradigms, including the ones inspired by the biological, social, and physical worlds. Self-* systems engineering: goals and requirements, hardware and software design, deployment, management and control, validation. Properties of self-* systems: self-organisation and emergent behaviour, self-adaptation, self-management, self-monitoring, self-tuning, self-repair, self-configuration, etc. Evaluation of self-* systems: methods for performance, robustness, and dependability assessment and analysis. Social self-* systems: emergent human behaviour, crowdsourcing, collective awareness, gamification and serious games. Applications and experiences with self-* systems: cyber security, transportation, computational sustainability, power systems, large networks, large data centers, and cloud computing. Submission Process ================= For evaluation and selection, authors should submit a two-page extended abstract of their poster. The format of this extended abstract must comply with the IEEE Computer Society Press proceedings style guide and it shall be submitted electronically in PDF format. Templates for Word and LaTeX are available at the conference site. Please register as authors and submit your papers using the SASO 2014 conference management system. Poster authors should use the poster track for their submissions. Accepted Posters ================ If selected, authors shall prepare a final, camera ready version of the extended abstract, taking into account all feedback from reviewers, and formatted according to the IEEE Computer Society Press proceedings style guide. Posters will be advertised in the final program, and authors' two-page extended abstracts will be published by the IEEE Computer Society Press as part of the conference proceedings. Abstracts will also be available as part of the IEEE Digital Library. Poster Content ============== Authors shall prepare their poster for presentation in the reserved poster session, taking into consideration that all posters should include the following information: - The purpose and goals of the work. - Any background and motivation needed to understand the work. - Any critical hypotheses and assumptions that underlie the work. - A clear summary of the contribution and/or results, in sufficient detail for a (re)viewer to understand the work and its relevance. If the work is at an initial stage, it is especially important to state clearly the anticipated contributions and any early results towards them. - The relationship to other related efforts, where appropriate. Authors of accepted posters may be asked to point out relationships to work represented by other accepted posters. - Where to find additional information This should include but is not restricted to: a web site where viewers can go to find additional information about the work how to contact the authors, including email addresses citations for any papers, books, or other materials that provide additional information. Poster Layout Guidelines ======================== The format of posters and the nature of poster sessions require authors to capture the viewers' attention effectively, and present core concepts so as to clearly position the context of their research work. For this reason, graphic representations, figures, and screen shots are typically the main medium of communication in successful posters. Few attendees will stop to read a large poster with dense text. If screen shots are used, please ensure that they print legibly and that the fonts are large enough to be read easily once printed. The recommended size for the poster is A0 and all poster authors are required to print and bring their posters at the conference. Attendance ========== At least one of the poster authors is required to register at the conference and will be required to give a brief presentation of the poster in the interactive poster session, as well as staying with the poster to discuss the work with conference attendees for the duration of the scheduled poster sessions. Contact details =============== For additional information, clarification, or questions, please contact the Poster Chairs. Iva Boji?, University of Zagreb, Croatia (iva.bojic at fer.hr) Regis Riveret, Imperial College, UK (r.riveret at imperial.ac.uk) -- |----------------------------------------------------| | Prof. Giacomo Cabri - Ph.D., Associate Professor | Dip. di Scienze Fisiche, Informatiche e Matematiche | Universita' di Modena e Reggio Emilia - Italia | e-mail giacomo.cabri at unimore.it | tel. +39-059-2058320 fax +39-059-2055216 |----------------------------------------------------| From weng at cse.msu.edu Thu Mar 13 20:38:23 2014 From: weng at cse.msu.edu (Juyang Weng) Date: Thu, 13 Mar 2014 20:38:23 -0400 Subject: Connectionists: how the brain works? In-Reply-To: <5319F22D.80609@gmail.com> References: <5319F22D.80609@gmail.com> Message-ID: <53224F7F.9010406@cse.msu.edu> Danko, Good attempt. Any theory about brain/mind must address the First Principle: How it learns visual invariance directly from natural cluttered environments. Your article does not seem to address the First Principle, does it? -John On 3/7/14 11:22 AM, Danko Nikolic wrote: > I believe that the readers of Connectionists list my be interested in > the manuscript available on arXiv (1402.5332) proposing the principles > by which adaptive systems create intelligent behavior. It is a > theoretical paper that has been recently submitted to a journal, and > the editors agreed to post it on arXiv. > > A nice context for this manuscript is, I think, the recent discussion > on Connectionists list on "how the brain works?", -- including the > comparison to how the radio works, arguments that neuroscience has not > reached the maturity of 19th century physics, that the development > should be an essential component, etc. > > I assess that anyone who enjoyed following that discussion, like I > did, would be interested also in what the proposed theory has to say. > > The theory addresses those problems by placing the question of brain > workings one level more abstract than it is usually discussed: It > proposes a general set of properties that adaptive systems need to > have to exhibit intelligent behavior (nevertheless, concrete examples > are given from biology and technology). Finally, the theory proposes > what is, in principle, missing in the current approaches in order to > account for the higher, biological-like levels of adaptive behavior. > > For those who are interested, I recommend using the link on my website: > > http://www.danko-nikolic.com/practopoiesis/ > > because there I provided, in addition, a simplified introduction into > some of the main conclusions derived from the theory. > > I would very much like to know what people think. Comments will be > appreciated. > > With warm greetings from Germany, > > Danko Nikolic > -- -- Juyang (John) Weng, Professor Department of Computer Science and Engineering MSU Cognitive Science Program and MSU Neuroscience Program 428 S Shaw Ln Rm 3115 Michigan State University East Lansing, MI 48824 USA Tel: 517-353-4388 Fax: 517-432-1061 Email: weng at cse.msu.edu URL: http://www.cse.msu.edu/~weng/ ---------------------------------------------- From brian.mingus at colorado.edu Thu Mar 13 21:40:44 2014 From: brian.mingus at colorado.edu (Brian J Mingus) Date: Thu, 13 Mar 2014 19:40:44 -0600 Subject: Connectionists: how the brain works? In-Reply-To: <53224F7F.9010406@cse.msu.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> Message-ID: Hi John, Theories of the brain will come in at multiple levels of abstraction. A reasonable first pass is to take object recognition as a given. It's clear that language and general intelligence doesn't require it. Hellen Keller is a great example - deaf and blind, and with patience, extremely intelligent. Visual and auditory object recognition simply aren't required! Brian On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng wrote: > Danko, > > Good attempt. > > Any theory about brain/mind must address the First Principle: How it > learns visual invariance directly from natural cluttered environments. > Your article does not seem to address the First Principle, does it? > > -John > > > On 3/7/14 11:22 AM, Danko Nikolic wrote: > >> I believe that the readers of Connectionists list my be interested in the >> manuscript available on arXiv (1402.5332) proposing the principles by which >> adaptive systems create intelligent behavior. It is a theoretical paper >> that has been recently submitted to a journal, and the editors agreed to >> post it on arXiv. >> >> A nice context for this manuscript is, I think, the recent discussion on >> Connectionists list on "how the brain works?", -- including the comparison >> to how the radio works, arguments that neuroscience has not reached the >> maturity of 19th century physics, that the development should be an >> essential component, etc. >> >> I assess that anyone who enjoyed following that discussion, like I did, >> would be interested also in what the proposed theory has to say. >> >> The theory addresses those problems by placing the question of brain >> workings one level more abstract than it is usually discussed: It proposes >> a general set of properties that adaptive systems need to have to exhibit >> intelligent behavior (nevertheless, concrete examples are given from >> biology and technology). Finally, the theory proposes what is, in >> principle, missing in the current approaches in order to account for the >> higher, biological-like levels of adaptive behavior. >> >> For those who are interested, I recommend using the link on my website: >> >> http://www.danko-nikolic.com/practopoiesis/ >> >> because there I provided, in addition, a simplified introduction into >> some of the main conclusions derived from the theory. >> >> I would very much like to know what people think. Comments will be >> appreciated. >> >> With warm greetings from Germany, >> >> Danko Nikolic >> >> > -- > -- > Juyang (John) Weng, Professor > Department of Computer Science and Engineering > MSU Cognitive Science Program and MSU Neuroscience Program > 428 S Shaw Ln Rm 3115 > Michigan State University > East Lansing, MI 48824 USA > Tel: 517-353-4388 > Fax: 517-432-1061 > Email: weng at cse.msu.edu > URL: http://www.cse.msu.edu/~weng/ > ---------------------------------------------- > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From brian.mingus at colorado.edu Thu Mar 13 22:30:36 2014 From: brian.mingus at colorado.edu (Brian J Mingus) Date: Thu, 13 Mar 2014 20:30:36 -0600 Subject: Connectionists: [SPAM]Re: how the brain works? In-Reply-To: <4AD8F84F0AA4E1448BD8131BA7E55EB41421C2C6@exmbt02.asurite.ad.asu.edu> References: <4AD8F84F0AA4E1448BD8131BA7E55EB41421C2C6@exmbt02.asurite.ad.asu.edu> Message-ID: Asim, Abstraction alone does not result in a being capable of language comprehension and production. For evidence, you can look at the variety of aphasias. It's clear that a very specific evolved architecture underlies language, and it is not just infinite abstraction that results in a single neuron that is invariant to everything (reductio ad absurdum). Responding specifically to John, claiming that the "first principle" of brain function is object recognition doesn't really seem to be justifiable. I can just as easily argue that we should start with the architecture underlying language or executive functioning, and then add in more details only as needed until the model passes my intelligence tests (i.e., reinventing consciousness philosophy). Brian On Thu, Mar 13, 2014 at 8:17 PM, Asim Roy wrote: > There is plenty of neurophysiological evidence that abstractions are > used in the brain - from the lowest (line orientation and other feature > detector cells) to the highest levels (multimodal object recognition, > complex abstract cells, place cells). Here are some references: > > > > A theory of the brain: localist representation is used widely in the brain > > > > An extension of the localist representation theory: grandmother cells are > also widely used in the brain > > > > Asim Roy > > Arizona State University > > http://lifeboat.com/ex/bios.asim.roy > > > > > > *From:* Connectionists [mailto: > connectionists-bounces at mailman.srv.cs.cmu.edu] *On Behalf Of *Brian J > Mingus > *Sent:* Thursday, March 13, 2014 6:41 PM > *To:* Juyang Weng > *Cc:* connectionists at mailman.srv.cs.cmu.edu > *Subject:* [SPAM]Re: Connectionists: how the brain works? > > > > Hi John, > > > > Theories of the brain will come in at multiple levels of abstraction. A > reasonable first pass is to take object recognition as a given. It's clear > that language and general intelligence doesn't require it. Hellen Keller is > a great example - deaf and blind, and with patience, extremely intelligent. > Visual and auditory object recognition simply aren't required! > > > > Brian > > > > > > > > On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng wrote: > > Danko, > > Good attempt. > > Any theory about brain/mind must address the First Principle: How it > learns visual invariance directly from natural cluttered environments. > Your article does not seem to address the First Principle, does it? > > -John > > > > On 3/7/14 11:22 AM, Danko Nikolic wrote: > > I believe that the readers of Connectionists list my be interested in the > manuscript available on arXiv (1402.5332) proposing the principles by which > adaptive systems create intelligent behavior. It is a theoretical paper > that has been recently submitted to a journal, and the editors agreed to > post it on arXiv. > > A nice context for this manuscript is, I think, the recent discussion on > Connectionists list on "how the brain works?", -- including the comparison > to how the radio works, arguments that neuroscience has not reached the > maturity of 19th century physics, that the development should be an > essential component, etc. > > I assess that anyone who enjoyed following that discussion, like I did, > would be interested also in what the proposed theory has to say. > > The theory addresses those problems by placing the question of brain > workings one level more abstract than it is usually discussed: It proposes > a general set of properties that adaptive systems need to have to exhibit > intelligent behavior (nevertheless, concrete examples are given from > biology and technology). Finally, the theory proposes what is, in > principle, missing in the current approaches in order to account for the > higher, biological-like levels of adaptive behavior. > > For those who are interested, I recommend using the link on my website: > > http://www.danko-nikolic.com/practopoiesis/ > > because there I provided, in addition, a simplified introduction into some > of the main conclusions derived from the theory. > > I would very much like to know what people think. Comments will be > appreciated. > > With warm greetings from Germany, > > Danko Nikolic > > > -- > > -- > Juyang (John) Weng, Professor > Department of Computer Science and Engineering > MSU Cognitive Science Program and MSU Neuroscience Program > 428 S Shaw Ln Rm 3115 > Michigan State University > East Lansing, MI 48824 USA > Tel: 517-353-4388 > Fax: 517-432-1061 > Email: weng at cse.msu.edu > URL: http://www.cse.msu.edu/~weng/ > ---------------------------------------------- > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From brian.mingus at colorado.edu Thu Mar 13 22:49:21 2014 From: brian.mingus at colorado.edu (Brian J Mingus) Date: Thu, 13 Mar 2014 20:49:21 -0600 Subject: Connectionists: [SPAM]Re: [SPAM]Re: how the brain works? In-Reply-To: <4AD8F84F0AA4E1448BD8131BA7E55EB41421C31C@exmbt02.asurite.ad.asu.edu> References: <4AD8F84F0AA4E1448BD8131BA7E55EB41421C31C@exmbt02.asurite.ad.asu.edu> Message-ID: Hi Asim, Abstract concepts such as "bird" do not need to be defined in terms of what birds look like or sound like, but can be defined in terms of what they feel like or smell like. This is the embodied perspective. More generally, though, we can define a bird in terms of other things, despite never having experienced them. While it seems hard to argue that some kind of embodied interaction with the world is necessary for intelligence, I can't personally argue that any specific sensory modality is required. As indicated by Hellen Keller, only smell and touch and taste are required, and probably just one of those is required (principally touch), but I could see smell and taste working as well. For this reason, we can skip object recognition and jump straight to IT cortex, where we find object invariant representations, and perhaps network representations of meaning akin to Latent Semantic Analysis. Indeed, perhaps a mind that has a coherent semantic network manually pre-trained in IT cortex and elsewhere can skip embodiment altogether, and jump straight to intelligence (assuming the rest of the architecture is coherent). This would not be unlike a sensory deprivation chamber. If you never had the senses in the first place, it wouldn't be deprivation. It would just be thinking and feeling. Brian On Thu, Mar 13, 2014 at 8:36 PM, Asim Roy wrote: > Brian, > > > > I did not mean infinite abstraction. But higher level complex abstractions > are definitely part of the architecture. > > > > Asim > > > > *From:* Brian J Mingus [mailto:brian.mingus at Colorado.EDU] > *Sent:* Thursday, March 13, 2014 7:31 PM > *To:* Asim Roy > *Cc:* Juyang Weng; connectionists at mailman.srv.cs.cmu.edu > *Subject:* [SPAM]Re: [SPAM]Re: Connectionists: how the brain works? > > > > Asim, > > > > Abstraction alone does not result in a being capable of language > comprehension and production. For evidence, you can look at the variety of > aphasias. It's clear that a very specific evolved architecture underlies > language, and it is not just infinite abstraction that results in a single > neuron that is invariant to everything (reductio ad absurdum). > > > > Responding specifically to John, claiming that the "first principle" of > brain function is object recognition doesn't really seem to be justifiable. > I can just as easily argue that we should start with the architecture > underlying language or executive functioning, and then add in more details > only as needed until the model passes my intelligence tests (i.e., > reinventing consciousness philosophy). > > > > Brian > > > > > > > > On Thu, Mar 13, 2014 at 8:17 PM, Asim Roy wrote: > > There is plenty of neurophysiological evidence that abstractions are used > in the brain - from the lowest (line orientation and other feature detector > cells) to the highest levels (multimodal object recognition, complex > abstract cells, place cells). Here are some references: > > > > A theory of the brain: localist representation is used widely in the brain > > > > An extension of the localist representation theory: grandmother cells are > also widely used in the brain > > > > Asim Roy > > Arizona State University > > http://lifeboat.com/ex/bios.asim.roy > > > > > > *From:* Connectionists [mailto: > connectionists-bounces at mailman.srv.cs.cmu.edu] *On Behalf Of *Brian J > Mingus > *Sent:* Thursday, March 13, 2014 6:41 PM > *To:* Juyang Weng > *Cc:* connectionists at mailman.srv.cs.cmu.edu > *Subject:* [SPAM]Re: Connectionists: how the brain works? > > > > Hi John, > > > > Theories of the brain will come in at multiple levels of abstraction. A > reasonable first pass is to take object recognition as a given. It's clear > that language and general intelligence doesn't require it. Hellen Keller is > a great example - deaf and blind, and with patience, extremely intelligent. > Visual and auditory object recognition simply aren't required! > > > > Brian > > > > > > > > On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng wrote: > > Danko, > > Good attempt. > > Any theory about brain/mind must address the First Principle: How it > learns visual invariance directly from natural cluttered environments. > Your article does not seem to address the First Principle, does it? > > -John > > > > On 3/7/14 11:22 AM, Danko Nikolic wrote: > > I believe that the readers of Connectionists list my be interested in the > manuscript available on arXiv (1402.5332) proposing the principles by which > adaptive systems create intelligent behavior. It is a theoretical paper > that has been recently submitted to a journal, and the editors agreed to > post it on arXiv. > > A nice context for this manuscript is, I think, the recent discussion on > Connectionists list on "how the brain works?", -- including the comparison > to how the radio works, arguments that neuroscience has not reached the > maturity of 19th century physics, that the development should be an > essential component, etc. > > I assess that anyone who enjoyed following that discussion, like I did, > would be interested also in what the proposed theory has to say. > > The theory addresses those problems by placing the question of brain > workings one level more abstract than it is usually discussed: It proposes > a general set of properties that adaptive systems need to have to exhibit > intelligent behavior (nevertheless, concrete examples are given from > biology and technology). Finally, the theory proposes what is, in > principle, missing in the current approaches in order to account for the > higher, biological-like levels of adaptive behavior. > > For those who are interested, I recommend using the link on my website: > > http://www.danko-nikolic.com/practopoiesis/ > > because there I provided, in addition, a simplified introduction into some > of the main conclusions derived from the theory. > > I would very much like to know what people think. Comments will be > appreciated. > > With warm greetings from Germany, > > Danko Nikolic > > > -- > > -- > Juyang (John) Weng, Professor > Department of Computer Science and Engineering > MSU Cognitive Science Program and MSU Neuroscience Program > 428 S Shaw Ln Rm 3115 > Michigan State University > East Lansing, MI 48824 USA > Tel: 517-353-4388 > Fax: 517-432-1061 > Email: weng at cse.msu.edu > URL: http://www.cse.msu.edu/~weng/ > ---------------------------------------------- > > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From itialife at gmail.com Wed Mar 12 19:37:16 2014 From: itialife at gmail.com (Keyan Ghazi-Zahedi) Date: Thu, 13 Mar 2014 00:37:16 +0100 Subject: Connectionists: Workshop on information theoretic incentives for artificial life Message-ID: *** WORKSHOP ON INFORMATION THEORETIC INCENTIVES FOR ARTIFICIAL LIFE *** to be held at the 14th International Conference on the Synthesis and Simulation of Living Systems (ALIFE-14) New York City, 30 July-2 August 2014 Workshop web page: http://www.mis.mpg.de/ay/workshops/alife14ws.html Conference web page: http://alife14.org Please forward this mail to other interested parties. ~~~ IMPORTANT DATES Submission deadline: 2 May 2014 Notification of acceptance: 23 May 2014 Workshop date: 30 July 2014 (to be confirmed, possible alternative is the 31st of July) ~~~ ABOUT Artificial Life aims to understand the basic and generic principles of life, and demonstrate this understanding by producing life-like systems based on those principles. In recent years, with the advent of the information age, and the widespread acceptance of information technology, our view of life has changed. Ideas such as "life is information processing" or "information holds the key to understanding life" have become more common. But what can information, or more formally Information Theory, offer to Artificial Life? One relevant area is the motivation of behaviour for artificial agents, both virtual and real. Instead of learning to perform a specific task, informational measures can be used to define concepts such as boredom, empowerment or the ability to predict one's own future. Intrinsic motivations derived from these concepts allow us to generate behaviour, ideally from an embodied and enactive perspective, which are based on basic but generic principles. The key questions here are: "What are the important intrinsic motivations a living agent has, and what behaviour can be produced by them?" Related to an agent's behaviour is also the question on how and where the necessary computation to realise this behaviour is performed. Can information be used to quantify the morphological computation of an embodied agent and to what degree are the computational limitations of an agent influencing its behaviour? Another area of interest is the guidance of artificial evolution or adaptation. Assuming it is true that an agent wants to optimise its information processing, possibly obtain as much relevant information as possible for the cheapest computational cost, then what behaviour would naturally follow from that? Can the development of social interaction or collective phenomena be motivated by an informational gradient? Furthermore, evolution itself can be seen as a process in which an agent population obtains information from the environment, which begs the question of how this can be quantified, and how systems would adapt to maximise this information? The common theme in those different scenarios is the identification and quantification of driving forces behind evolution, learning, behaviour and other crucial processes of life, in the hope that the implementation or optimisation of these measurements will allow us to construct life-like systems. ~~~ WORKSHOP FORMAT The workshop is scheduled for a full day, and will consist of a combination of keynotes, presentations, discussions and shorter student presentations. To participate in the workshop by giving a presentation we would ask you to submit an extended abstract. We are interested in both, the current existing approaches in the field, and possible future avenues of investigation. Student presentations are an option for younger researchers, new to the field, that would like to outline and discuss their research direction or early results. The general idea is to offer a forum for those interested in applying information theory and similar methods to the field of artificial life, to have a focused discussion on both, the possibilities and technical challenges involved. After the workshop, there will a special issue in the Journal "Entropy" on the topic of the workshop, and we encourage participants to submit an extended version of their work. Further details will be announced as soon as possible. ~~~ SUBMISSION AND PARTICIPATION How to submit If you want to participate in the workshop by giving a talk we would invite you to send us an email (itialife at gmail.com) by 2 May 2014 with - name - contact details -1-2 pages long extended abstract. We are interested in previous work related to the subject and current work, including preliminary results. Specifically for students we also offer the option to submit for a shorter student talk, to present some early results, and discuss their approach to the field. In this case, please submit a 1-2 pages long extended abstract and indicate that you are interested in a student presentation. If there are any questions, or if you just want to indicate interest in submitting or attending, please feel free to mail us at itialife at gmail.com. ~~~ CONTACTS Web: http://www.mis.mpg.de/ay/workshops/alife14ws.html Email: itialife at gmail.com ~~~ Organisers: Christoph Salge, Keyan Zahedi, Georg Martius and Daniel Polani With best wishes, Keyan Zahedi, on behalf of the organisers -- Keyan Ghazi-Zahedi, Dr. rer. nat. Information Theory of Cognitive Systems Max Planck Institute for Mathematics in the Sciences phone: (+49) 341 9959 545, fax: (+49) 341 9959 555, office A11 From stefano.panzeri at gmail.com Thu Mar 13 02:26:03 2014 From: stefano.panzeri at gmail.com (Stefano Panzeri) Date: Thu, 13 Mar 2014 07:26:03 +0100 Subject: Connectionists: Postdoctoral position in computational neuroscience at IIT-CNCS - Laboratory of Neural Computation, Rovereto, Italy Message-ID: Postdoctoral position in computational neuroscience at IIT-CNCS - Laboratory of Neural Computation, Rovereto, Italy Applications are sought for a post-doctoral researcher to work in computational neuroscience. The postdoctoral candidates will be working in the Centre for Neuroscience and Cognitive Systems in Trento (Italy) under the supervision of Prof. S. Panzeri. The postdoc will contribute to a project entitled "VISUAL MODELLING USING GANGLION CELLS". The project, funded by the European Union Future Emerging Technology programme, will develop a set of novel information theoretic algorithms for the analysis of simultaneous recordings from large populations of retinal ganglion cells under dynamic natural visual stimulation. These algorithms will be applied to analyse the functional response properties of these cells and will expose new principles of spike timing encoding that bridge the gap between single cell and population information processing. The project will involve collaborations with a retinal neurophysiology laboratory at Gottingen University (Prof Tim Gollish), as well as collaborations with neuromorphic electronic engineers and roboticists (University of Zurich, and University of Ulster) to contribute to the development of novel theoretical and hardware models of biological retinal ganglion cell types for dynamic vision applications. The ideal candidates will have a strong background in numerate sciences (physics, mathematics, engineering or informatics) and a keen interest in applying their numerate background to make breakthroughs in the understanding of the brain. Proficiency in English is required. The postdoctoral researchers will work at the IIT (CNCS at UNITN) most of the time under the superivision of internationally recognized experts and will have access to state-of-the-art equipment and laboratories. Interested applicants should contact Stefano Panzeri ( stefano.panzeri at iit.it ) for any informal queries. For a formal application, the candidate will submit his/her CV, list of publications, a statement of research interests and the names and email addresses of two referees by e-mail to both stefano.panzeri at iit.it and Sara Maistrelli ( Cncs_selezioni at iit.it ). Please quote REF CB 69468 in the application. Deadline for the application is April 6, 2014. For recent reviews about the work carried out by the laboratory of Neural Computation, prospective applicants are invited to consult the following articles: G.T. Einevoll, C. Kayser, N.K. Logothetis and S. Panzeri (2013) Modelling and analysis of local field potential for studying the function of cortical circuits. Nature Reviews Neuroscience, 14:770-785; Panzeri S, Brunel N, Logothetis NK, Kayser C (2010) Neural codes at multiple temporal scales in sensory cortex.Trends in Neuroscience 33: 111-120 Quian Quiroga R, Panzeri S (2009) Extracting information from neuronal populations: information theory and decoding approaches. Nature Reviews Neuroscience 10: 173-185. The formal full job post advert can be found at: http://www.iit.it/en/openings/iit-centres/iit-unitn-openings/2331-postdoctoral-positions-in-computational-neuroscience-at-iit-cncs-laboratory-of-neural-computation-rovereto-italy.html -------------- next part -------------- An HTML attachment was scrubbed... URL: From Muhammad.Iqbal at ecs.vuw.ac.nz Fri Mar 14 00:16:39 2014 From: Muhammad.Iqbal at ecs.vuw.ac.nz (Muhammad.Iqbal at ecs.vuw.ac.nz) Date: Fri, 14 Mar 2014 17:16:39 +1300 Subject: Connectionists: Call for Papers - IWLCS 2014 Message-ID: <0177d82e6e9c8e3f82eb4eaa686d3a1b.squirrel@mail.ecs.vuw.ac.nz> Dear colleague, The Seventeenth International Workshop on Learning Classifier Systems (IWLCS 2014) will be held in Vancouver, BC, Canada during the Genetic and Evolutionary Computation Conference (GECCO-2014), July 12-16, 2014. We invite submissions that discuss recent developments in all areas of research on, and applications of, Learning Classifier Systems. IWLCS is the event that brings together most of the core researchers in classifier systems. The workshop also provides an opportunity for researchers interested in LCSs to get an impression of the current research directions in the field as well as a guideline for the application of LCSs to their problem domain. For more details, please visit the IWLCS'14 URL: http://homepages.ecs.vuw.ac.nz/~iqbal/iwlcs2014/index.html Submission and Publication: Submissions will be short-papers up to 8 pages in ACM format. Please see the GECCO 2014 information for authors for further details. However, unlike GECCO, papers do not have to be submitted in anonymous format. All accepted papers will be presented at IWLCS 2014 and will appear in the GECCO workshop volume, which will be published by ACM (Association for Computing Machinery). Authors will be invited after the workshop to submit revised (full) papers that, after a thorough review process, are to be published in a special issue of the Evolutionary Intelligence journal. All papers should be submitted in PDF format and e-mailed to: iwlcssubmissions at gmail.com Important dates: March 28, 2014 - Paper submission deadline April 15, 2014 - Notification to authors April 29, 2014 - Submission of camera-ready material July 12-16, 2014 - GECCO 2014 Conference in Vancouver, BC, Canada Regards, IWLCS'14 Organizing Committee Muhammad Iqbal, Victoria University of Wellington, New Zealand. (muhammad.iqbal at ecs.vuw.ac.nz) Kamran Shafi, University of New South Wales, Australia. (k.shafi at adfa.edu.au) Ryan Urbanowicz, Dartmouth College, USA. (ryan.j.urbanowicz at dartmouth.edu) From ASIM.ROY at asu.edu Fri Mar 14 00:16:04 2014 From: ASIM.ROY at asu.edu (Asim Roy) Date: Fri, 14 Mar 2014 04:16:04 +0000 Subject: Connectionists: how the brain works? Message-ID: <4AD8F84F0AA4E1448BD8131BA7E55EB41421C3A1@exmbt02.asurite.ad.asu.edu> There is plenty of neurophysiological evidence that abstractions are used in the brain - from the lowest (line orientation and other feature detector cells) to the highest levels (multimodal object recognition, complex abstract cells, place cells). Here are some references: A theory of the brain: localist representation is used widely in the brain An extension of the localist representation theory: grandmother cells are also widely used in the brain Asim Roy Arizona State University http://lifeboat.com/ex/bios.asim.roy From: Connectionists [mailto:connectionists-bounces at mailman.srv.cs.cmu.edu] On Behalf Of Brian J Mingus Sent: Thursday, March 13, 2014 6:41 PM To: Juyang Weng Cc: connectionists at mailman.srv.cs.cmu.edu Subject: [SPAM]Re: Connectionists: how the brain works? Hi John, Theories of the brain will come in at multiple levels of abstraction. A reasonable first pass is to take object recognition as a given. It's clear that language and general intelligence doesn't require it. Hellen Keller is a great example - deaf and blind, and with patience, extremely intelligent. Visual and auditory object recognition simply aren't required! Brian On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng > wrote: Danko, Good attempt. Any theory about brain/mind must address the First Principle: How it learns visual invariance directly from natural cluttered environments. Your article does not seem to address the First Principle, does it? -John On 3/7/14 11:22 AM, Danko Nikolic wrote: I believe that the readers of Connectionists list my be interested in the manuscript available on arXiv (1402.5332) proposing the principles by which adaptive systems create intelligent behavior. It is a theoretical paper that has been recently submitted to a journal, and the editors agreed to post it on arXiv. A nice context for this manuscript is, I think, the recent discussion on Connectionists list on "how the brain works?", -- including the comparison to how the radio works, arguments that neuroscience has not reached the maturity of 19th century physics, that the development should be an essential component, etc. I assess that anyone who enjoyed following that discussion, like I did, would be interested also in what the proposed theory has to say. The theory addresses those problems by placing the question of brain workings one level more abstract than it is usually discussed: It proposes a general set of properties that adaptive systems need to have to exhibit intelligent behavior (nevertheless, concrete examples are given from biology and technology). Finally, the theory proposes what is, in principle, missing in the current approaches in order to account for the higher, biological-like levels of adaptive behavior. For those who are interested, I recommend using the link on my website: http://www.danko-nikolic.com/practopoiesis/ because there I provided, in addition, a simplified introduction into some of the main conclusions derived from the theory. I would very much like to know what people think. Comments will be appreciated. With warm greetings from Germany, Danko Nikolic -- -- Juyang (John) Weng, Professor Department of Computer Science and Engineering MSU Cognitive Science Program and MSU Neuroscience Program 428 S Shaw Ln Rm 3115 Michigan State University East Lansing, MI 48824 USA Tel: 517-353-4388 Fax: 517-432-1061 Email: weng at cse.msu.edu URL: http://www.cse.msu.edu/~weng/ ---------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From aberg at cs.unc.edu Fri Mar 14 00:28:17 2014 From: aberg at cs.unc.edu (Alex Berg) Date: Fri, 14 Mar 2014 00:28:17 -0400 Subject: Connectionists: BigVision 2014: Big Data Meets Computer Vision Message-ID: https://sites.google.com/site/bigvision2014cvpr/ The goal of this workshop is providing a venue for researchers interested in large-scale vision to present new work, exchange ideas, and build connections. The workshop will feature keynotes and invited talks from prominent researchers as well as a poster session that fosters in depth discussion. We invite submissions of extended abstracts related to the following topics in the context of big data and large-scale vision: Indexing algorithms and data structures Weakly supervised or unsupervised learning Never ending / continuous learning Metric learning Visual models and feature representations Transfer learning and domain adaptation Systems and infrastructure Visual data mining and knowledge discovery Dataset issues (e.g. dataset collection and dataset biases) Efficient learning and inference techniques Optimization techniques The abstracts should be no more than 2 pages in CVPR 2014 format. Accepted abstracts will be presented as posters or oral talks. The workshop is not intended as a venue for publication and no proceedings will be produced. All submissions will undergo double-blind reviews. In the case of previous published work, the review will be single-blind. -------------- next part -------------- An HTML attachment was scrubbed... URL: From ASIM.ROY at asu.edu Fri Mar 14 00:15:12 2014 From: ASIM.ROY at asu.edu (Asim Roy) Date: Fri, 14 Mar 2014 04:15:12 +0000 Subject: Connectionists: how the brain works? Message-ID: <4AD8F84F0AA4E1448BD8131BA7E55EB41421C38B@exmbt02.asurite.ad.asu.edu> Brian, I did not mean infinite abstraction. But higher level complex abstractions are definitely part of the architecture. Asim From: Brian J Mingus [mailto:brian.mingus at Colorado.EDU] Sent: Thursday, March 13, 2014 7:31 PM To: Asim Roy Cc: Juyang Weng; connectionists at mailman.srv.cs.cmu.edu Subject: [SPAM]Re: [SPAM]Re: Connectionists: how the brain works? Asim, Abstraction alone does not result in a being capable of language comprehension and production. For evidence, you can look at the variety of aphasias. It's clear that a very specific evolved architecture underlies language, and it is not just infinite abstraction that results in a single neuron that is invariant to everything (reductio ad absurdum). Responding specifically to John, claiming that the "first principle" of brain function is object recognition doesn't really seem to be justifiable. I can just as easily argue that we should start with the architecture underlying language or executive functioning, and then add in more details only as needed until the model passes my intelligence tests (i.e., reinventing consciousness philosophy). Brian On Thu, Mar 13, 2014 at 8:17 PM, Asim Roy > wrote: There is plenty of neurophysiological evidence that abstractions are used in the brain - from the lowest (line orientation and other feature detector cells) to the highest levels (multimodal object recognition, complex abstract cells, place cells). Here are some references: A theory of the brain: localist representation is used widely in the brain An extension of the localist representation theory: grandmother cells are also widely used in the brain Asim Roy Arizona State University http://lifeboat.com/ex/bios.asim.roy From: Connectionists [mailto:connectionists-bounces at mailman.srv.cs.cmu.edu] On Behalf Of Brian J Mingus Sent: Thursday, March 13, 2014 6:41 PM To: Juyang Weng Cc: connectionists at mailman.srv.cs.cmu.edu Subject: [SPAM]Re: Connectionists: how the brain works? Hi John, Theories of the brain will come in at multiple levels of abstraction. A reasonable first pass is to take object recognition as a given. It's clear that language and general intelligence doesn't require it. Hellen Keller is a great example - deaf and blind, and with patience, extremely intelligent. Visual and auditory object recognition simply aren't required! Brian On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng > wrote: Danko, Good attempt. Any theory about brain/mind must address the First Principle: How it learns visual invariance directly from natural cluttered environments. Your article does not seem to address the First Principle, does it? -John On 3/7/14 11:22 AM, Danko Nikolic wrote: I believe that the readers of Connectionists list my be interested in the manuscript available on arXiv (1402.5332) proposing the principles by which adaptive systems create intelligent behavior. It is a theoretical paper that has been recently submitted to a journal, and the editors agreed to post it on arXiv. A nice context for this manuscript is, I think, the recent discussion on Connectionists list on "how the brain works?", -- including the comparison to how the radio works, arguments that neuroscience has not reached the maturity of 19th century physics, that the development should be an essential component, etc. I assess that anyone who enjoyed following that discussion, like I did, would be interested also in what the proposed theory has to say. The theory addresses those problems by placing the question of brain workings one level more abstract than it is usually discussed: It proposes a general set of properties that adaptive systems need to have to exhibit intelligent behavior (nevertheless, concrete examples are given from biology and technology). Finally, the theory proposes what is, in principle, missing in the current approaches in order to account for the higher, biological-like levels of adaptive behavior. For those who are interested, I recommend using the link on my website: http://www.danko-nikolic.com/practopoiesis/ because there I provided, in addition, a simplified introduction into some of the main conclusions derived from the theory. I would very much like to know what people think. Comments will be appreciated. With warm greetings from Germany, Danko Nikolic -- -- Juyang (John) Weng, Professor Department of Computer Science and Engineering MSU Cognitive Science Program and MSU Neuroscience Program 428 S Shaw Ln Rm 3115 Michigan State University East Lansing, MI 48824 USA Tel: 517-353-4388 Fax: 517-432-1061 Email: weng at cse.msu.edu URL: http://www.cse.msu.edu/~weng/ ---------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From dchau at cs.cmu.edu Fri Mar 14 02:25:41 2014 From: dchau at cs.cmu.edu (Polo Chau) Date: Fri, 14 Mar 2014 02:25:41 -0400 Subject: Connectionists: Last call for KDD'14 tutorial proposals (due 3/15) Message-ID: Dear friends and colleagues, Last call for KDD'14 tutorial proposals (due 3/15)! Don't miss the chance to reach out to thousands of data scientists, researchers, practitioners, students and more. KDD'14, a top data science conference, will be held in New York City, August 24-27, 2014. To submit a proposal (due 3/15), visit: http://www.kdd.org/kdd2014/calls.html Cheers, Polo Chau, Kaitlin Atkinson, Ankur Teredesai KDD'14 Publicity and Media Chairs From danko.nikolic at googlemail.com Fri Mar 14 04:42:28 2014 From: danko.nikolic at googlemail.com (Danko Nikolic) Date: Fri, 14 Mar 2014 09:42:28 +0100 Subject: Connectionists: how the brain works? In-Reply-To: <53224F7F.9010406@cse.msu.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> Message-ID: <5322C0F4.5090608@gmail.com> Dear John, I apologize for a slow reply. It is true that the article does not discuss visual invariance explicitly. However, practopoietic theory, as it is a general theory of adaptive organization and intelligent behavior must, of course, have something to say about perceptual invariance. In practopoietic theory, perceptual invariance should be treated equally to any other aspect of intelligent behavior. Perceptual invariance is a form of intelligence in action, much like any other intelligence in action (catching a pray). Thus, perceptual invariance should rely on the same mechanisms as for example, a solution to a mathematical problem. The key mechanism here is anapoiesis of a T_3 system, which is a process for knowledge reconstruction (introduced in the second part of the manuscript). Anapoiesis has the inherent capability to create new knowledge, and this enables the system to recognize invariantly an object, even in novel situations/perspectives, never encountered before. In other words, object recognition is a logical abduction much like any other logical abduction. There is no first a stage of perceiving (at e.g., one level) and then a stage of thinking (at another level): Instead, perceiving is thinking. Thiking is perceiving. The two rely on the same mechanisms and principles. In conclusion, it follows from practopoietic theory that the classical division into perception-first-thinking-next is not entirely accurate and, if we want to understand how the brain works as a whole, we have to actually give up this division. This means also that the mechanisms of perceptual invariance do not belong to some sort of a first stage of a processing pipeline, the result then being passed onto the next stage. Rather, the two occur at the same level of system organization and cannot be pulled apart. In other words, to perceive invariantly, you already have to be quite intelligent. With best regards, Danko On 3/14/14 1:38 AM, Juyang Weng wrote: > Danko, > > Good attempt. > > Any theory about brain/mind must address the First Principle: How it > learns visual invariance directly from natural cluttered environments. > Your article does not seem to address the First Principle, does it? > > -John > > On 3/7/14 11:22 AM, Danko Nikolic wrote: >> I believe that the readers of Connectionists list my be interested in >> the manuscript available on arXiv (1402.5332) proposing the >> principles by which adaptive systems create intelligent behavior. It >> is a theoretical paper that has been recently submitted to a journal, >> and the editors agreed to post it on arXiv. >> >> A nice context for this manuscript is, I think, the recent discussion >> on Connectionists list on "how the brain works?", -- including the >> comparison to how the radio works, arguments that neuroscience has >> not reached the maturity of 19th century physics, that the >> development should be an essential component, etc. >> >> I assess that anyone who enjoyed following that discussion, like I >> did, would be interested also in what the proposed theory has to say. >> >> The theory addresses those problems by placing the question of brain >> workings one level more abstract than it is usually discussed: It >> proposes a general set of properties that adaptive systems need to >> have to exhibit intelligent behavior (nevertheless, concrete examples >> are given from biology and technology). Finally, the theory proposes >> what is, in principle, missing in the current approaches in order to >> account for the higher, biological-like levels of adaptive behavior. >> >> For those who are interested, I recommend using the link on my website: >> >> http://www.danko-nikolic.com/practopoiesis/ >> >> because there I provided, in addition, a simplified introduction into >> some of the main conclusions derived from the theory. >> >> I would very much like to know what people think. Comments will be >> appreciated. >> >> With warm greetings from Germany, >> >> Danko Nikolic >> > -- Danko Nikolic, Ph.D. Web: http://www.danko-nikolic.com Mail address 1: Department of Neurophysiology Max Planck Institut for Brain Research Deutschordenstr. 46 60528 Frankfurt am Main GERMANY Mail address 2: Frankfurt Institute for Advanced Studies Wolfgang Goethe University Ruth-Moufang-Str. 1 60433 Frankfurt am Main GERMANY ---------------------------- Office: (..49-69) 96769-736 Lab: (..49-69) 96769-209 Fax: (..49-69) 96769-327 danko.nikolic at gmail.com ---------------------------- From danko.nikolic at googlemail.com Fri Mar 14 04:45:45 2014 From: danko.nikolic at googlemail.com (Danko Nikolic) Date: Fri, 14 Mar 2014 09:45:45 +0100 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> Message-ID: <5322C1B9.2090304@gmail.com> Dear Brian, I agree. I think that practopoietic theory gives a general answer why it is so. Why general intelligence does not require a specific type of sensory information? The answer is that the mechanisms of generating intelligent adaptive behavior are the ones that have the control over how we use the sensory inputs--not the other way around. In other words, according to practopoiesis, biological systems are largely driven and controlled internally, rather then being driven by sensory inputs. This allows the internal systms to exhibit intelligence, language, etc. without specific sensory inputs. Of course, we need some inputs in order to learn about the world and develop the internal systems. But it does not matter which inputs as long as the ones that we have access too are informative enough about the environment. Regards, Danko On 3/14/14 2:40 AM, Brian J Mingus wrote: > Hi John, > > Theories of the brain will come in at multiple levels of abstraction. > A reasonable first pass is to take object recognition as a given. It's > clear that language and general intelligence doesn't require it. > Hellen Keller is a great example - deaf and blind, and with patience, > extremely intelligent. Visual and auditory object recognition simply > aren't required! > > Brian > > > > > On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng > wrote: > > Danko, > > Good attempt. > > Any theory about brain/mind must address the First Principle: How > it learns visual invariance directly from natural cluttered > environments. > Your article does not seem to address the First Principle, does it? > > -John > > > On 3/7/14 11:22 AM, Danko Nikolic wrote: > > I believe that the readers of Connectionists list my be > interested in the manuscript available on arXiv (1402.5332) > proposing the principles by which adaptive systems create > intelligent behavior. It is a theoretical paper that has been > recently submitted to a journal, and the editors agreed to > post it on arXiv. > > A nice context for this manuscript is, I think, the recent > discussion on Connectionists list on "how the brain works?", > -- including the comparison to how the radio works, arguments > that neuroscience has not reached the maturity of 19th century > physics, that the development should be an essential > component, etc. > > I assess that anyone who enjoyed following that discussion, > like I did, would be interested also in what the proposed > theory has to say. > > The theory addresses those problems by placing the question of > brain workings one level more abstract than it is usually > discussed: It proposes a general set of properties that > adaptive systems need to have to exhibit intelligent behavior > (nevertheless, concrete examples are given from biology and > technology). Finally, the theory proposes what is, in > principle, missing in the current approaches in order to > account for the higher, biological-like levels of adaptive > behavior. > > For those who are interested, I recommend using the link on my > website: > > http://www.danko-nikolic.com/practopoiesis/ > > because there I provided, in addition, a simplified > introduction into some of the main conclusions derived from > the theory. > > I would very much like to know what people think. Comments > will be appreciated. > > With warm greetings from Germany, > > Danko Nikolic > > > -- > -- > Juyang (John) Weng, Professor > Department of Computer Science and Engineering > MSU Cognitive Science Program and MSU Neuroscience Program > 428 S Shaw Ln Rm 3115 > Michigan State University > East Lansing, MI 48824 USA > Tel: 517-353-4388 > Fax: 517-432-1061 > Email: weng at cse.msu.edu > URL: http://www.cse.msu.edu/~weng/ > ---------------------------------------------- > > -- Danko Nikolic, Ph.D. Web: http://www.danko-nikolic.com Mail address 1: Department of Neurophysiology Max Planck Institut for Brain Research Deutschordenstr. 46 60528 Frankfurt am Main GERMANY Mail address 2: Frankfurt Institute for Advanced Studies Wolfgang Goethe University Ruth-Moufang-Str. 1 60433 Frankfurt am Main GERMANY ---------------------------- Office: (..49-69) 96769-736 Lab: (..49-69) 96769-209 Fax: (..49-69) 96769-327 danko.nikolic at gmail.com ---------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From danko.nikolic at googlemail.com Fri Mar 14 05:10:17 2014 From: danko.nikolic at googlemail.com (Danko Nikolic) Date: Fri, 14 Mar 2014 10:10:17 +0100 Subject: Connectionists: [SPAM]Re: [SPAM]Re: how the brain works? In-Reply-To: References: <4AD8F84F0AA4E1448BD8131BA7E55EB41421C31C@exmbt02.asurite.ad.asu.edu> Message-ID: <5322C779.3050308@gmail.com> Dear Brian, > Indeed, perhaps a mind that has a coherent semantic network manually > pre-trained in IT cortex and elsewhere can skip embodiment altogether, > and jump straight to intelligence (assuming the rest of the > architecture is coherent). Actually, according to practopoietic theory, it would not work for the reason that the system needs ultimately to close the sensory-motor loops as a way of re-checking the functionality of its abstract semantic networks. Without a constant sensory-motor feedback, the semantic parts would quickly go astray--becoming a kind of psychotic, much like in dreams, sensory deprivation, phantom limbs, etc. One can think about it in terms of development: A development of a brain (or an organism in general) is not possible without an interaction with the environment. The adult brain then works in the same way as a toddler brain but just with a smaller rate of change. This means that all our adult learning, thinking, and intelligent behavior requires in the same way interaction with the environment as it did when we were babies. Only computer algorithms can work in isolation (in the manuscript on practopoiesis I use the example of factoring a number). Our adaptive biological brains cannot do that. And this is a good news because exactly this dependence on the interaction with the environment is what enables us to become more adaptive and more intelligent than machines. With regards, Danko -- Danko Nikolic, Ph.D. Web: http://www.danko-nikolic.com Mail address 1: Department of Neurophysiology Max Planck Institut for Brain Research Deutschordenstr. 46 60528 Frankfurt am Main GERMANY Mail address 2: Frankfurt Institute for Advanced Studies Wolfgang Goethe University Ruth-Moufang-Str. 1 60433 Frankfurt am Main GERMANY ---------------------------- Office: (..49-69) 96769-736 Lab: (..49-69) 96769-209 Fax: (..49-69) 96769-327 danko.nikolic at gmail.com ---------------------------- From weng at cse.msu.edu Fri Mar 14 14:17:14 2014 From: weng at cse.msu.edu (Juyang Weng) Date: Fri, 14 Mar 2014 14:17:14 -0400 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> Message-ID: <532347AA.2020004@cse.msu.edu> > It's clear that language and general intelligence doesn't require it. This is clearly wrong if you know and understand our DN. I believe that any brain theory will miss the boat if it cannot explain the First Principle. The brain is not just an information processor, it is first a developer for the information processor. If one does not understand how the information processor develops, he definitely misses the boat in explaining how the brain processes information. That is why although "theories of the brain will come in at multiple levels of abstraction", they may miss the boat. The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). -John On 3/13/14 9:40 PM, Brian J Mingus wrote: > Hi John, > > Theories of the brain will come in at multiple levels of abstraction. > A reasonable first pass is to take object recognition as a given. It's > clear that language and general intelligence doesn't require it. > Hellen Keller is a great example - deaf and blind, and with patience, > extremely intelligent. Visual and auditory object recognition simply > aren't required! > > Brian > > > > > On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng > wrote: > > Danko, > > Good attempt. > > Any theory about brain/mind must address the First Principle: How > it learns visual invariance directly from natural cluttered > environments. > Your article does not seem to address the First Principle, does it? > > -John > > > On 3/7/14 11:22 AM, Danko Nikolic wrote: > > I believe that the readers of Connectionists list my be > interested in the manuscript available on arXiv (1402.5332) > proposing the principles by which adaptive systems create > intelligent behavior. It is a theoretical paper that has been > recently submitted to a journal, and the editors agreed to > post it on arXiv. > > A nice context for this manuscript is, I think, the recent > discussion on Connectionists list on "how the brain works?", > -- including the comparison to how the radio works, arguments > that neuroscience has not reached the maturity of 19th century > physics, that the development should be an essential > component, etc. > > I assess that anyone who enjoyed following that discussion, > like I did, would be interested also in what the proposed > theory has to say. > > The theory addresses those problems by placing the question of > brain workings one level more abstract than it is usually > discussed: It proposes a general set of properties that > adaptive systems need to have to exhibit intelligent behavior > (nevertheless, concrete examples are given from biology and > technology). Finally, the theory proposes what is, in > principle, missing in the current approaches in order to > account for the higher, biological-like levels of adaptive > behavior. > > For those who are interested, I recommend using the link on my > website: > > http://www.danko-nikolic.com/practopoiesis/ > > because there I provided, in addition, a simplified > introduction into some of the main conclusions derived from > the theory. > > I would very much like to know what people think. Comments > will be appreciated. > > With warm greetings from Germany, > > Danko Nikolic > > > -- > -- > Juyang (John) Weng, Professor > Department of Computer Science and Engineering > MSU Cognitive Science Program and MSU Neuroscience Program > 428 S Shaw Ln Rm 3115 > Michigan State University > East Lansing, MI 48824 USA > Tel: 517-353-4388 > Fax: 517-432-1061 > Email: weng at cse.msu.edu > URL: http://www.cse.msu.edu/~weng/ > ---------------------------------------------- > > -- -- Juyang (John) Weng, Professor Department of Computer Science and Engineering MSU Cognitive Science Program and MSU Neuroscience Program 428 S Shaw Ln Rm 3115 Michigan State University East Lansing, MI 48824 USA Tel: 517-353-4388 Fax: 517-432-1061 Email: weng at cse.msu.edu URL: http://www.cse.msu.edu/~weng/ ---------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From arbib at usc.edu Fri Mar 14 15:40:13 2014 From: arbib at usc.edu (Michael Arbib) Date: Fri, 14 Mar 2014 12:40:13 -0700 Subject: Connectionists: how the brain works? In-Reply-To: <532347AA.2020004@cse.msu.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> Message-ID: <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> An HTML attachment was scrubbed... URL: From ASIM.ROY at asu.edu Fri Mar 14 03:47:51 2014 From: ASIM.ROY at asu.edu (Asim Roy) Date: Fri, 14 Mar 2014 07:47:51 +0000 Subject: Connectionists: how the brain works? Message-ID: <4AD8F84F0AA4E1448BD8131BA7E55EB41421C477@exmbt02.asurite.ad.asu.edu> Hi Brian, Take tea tasters for example. The abstract category and pricing system in their heads is mainly based on taste and fragrance. So yes, some of these abstract concepts could be based on just one of the senses if that's what you mean. I have a paper under review with the title: "A theory of the brain: The most compact and easily accessible form of semantic knowledge exists in networks of abstract concept and category cells." Here is one of the concluding paragraphs from that paper: "There is obviously an efficiency aspect to storage of semantic information at this abstract level. It provides easy and quick access to cognitive-level information, information that is directly interpretable and has meaning at a higher level of thought. The physical embodiment of cognitive level information within a set of abstract concept cells makes cognition and thought very real and easily tractable within the brain. Thus simplification, concreteness, automation and computational efficiency are the key advantages of semantic knowledge stored at the abstract level." So compact semantic networks in the IT cortex and elsewhere are entirely feasible, although they are built in a bottom-up fashion based on what we learn over time. You are talking about a top-down process. To build artificial systems, you can justify a top-down process. A semantic network based on abstract concepts has plenty of neurophysiological evidence. Just think of place cells for navigation (which are, of course, abstract concept cells) and how they might be connected. Asim From: Brian J Mingus [mailto:brian.mingus at Colorado.EDU] Sent: Thursday, March 13, 2014 7:49 PM To: Asim Roy Cc: Juyang Weng; connectionists at mailman.srv.cs.cmu.edu Subject: [SPAM]Re: [SPAM]Re: [SPAM]Re: Connectionists: how the brain works? Hi Asim, Abstract concepts such as "bird" do not need to be defined in terms of what birds look like or sound like, but can be defined in terms of what they feel like or smell like. This is the embodied perspective. More generally, though, we can define a bird in terms of other things, despite never having experienced them. While it seems hard to argue that some kind of embodied interaction with the world is necessary for intelligence, I can't personally argue that any specific sensory modality is required. As indicated by Hellen Keller, only smell and touch and taste are required, and probably just one of those is required (principally touch), but I could see smell and taste working as well. For this reason, we can skip object recognition and jump straight to IT cortex, where we find object invariant representations, and perhaps network representations of meaning akin to Latent Semantic Analysis. Indeed, perhaps a mind that has a coherent semantic network manually pre-trained in IT cortex and elsewhere can skip embodiment altogether, and jump straight to intelligence (assuming the rest of the architecture is coherent). This would not be unlike a sensory deprivation chamber. If you never had the senses in the first place, it wouldn't be deprivation. It would just be thinking and feeling. Brian On Thu, Mar 13, 2014 at 8:36 PM, Asim Roy > wrote: Brian, I did not mean infinite abstraction. But higher level complex abstractions are definitely part of the architecture. Asim From: Brian J Mingus [mailto:brian.mingus at Colorado.EDU] Sent: Thursday, March 13, 2014 7:31 PM To: Asim Roy Cc: Juyang Weng; connectionists at mailman.srv.cs.cmu.edu Subject: [SPAM]Re: [SPAM]Re: Connectionists: how the brain works? Asim, Abstraction alone does not result in a being capable of language comprehension and production. For evidence, you can look at the variety of aphasias. It's clear that a very specific evolved architecture underlies language, and it is not just infinite abstraction that results in a single neuron that is invariant to everything (reductio ad absurdum). Responding specifically to John, claiming that the "first principle" of brain function is object recognition doesn't really seem to be justifiable. I can just as easily argue that we should start with the architecture underlying language or executive functioning, and then add in more details only as needed until the model passes my intelligence tests (i.e., reinventing consciousness philosophy). Brian On Thu, Mar 13, 2014 at 8:17 PM, Asim Roy > wrote: There is plenty of neurophysiological evidence that abstractions are used in the brain - from the lowest (line orientation and other feature detector cells) to the highest levels (multimodal object recognition, complex abstract cells, place cells). Here are some references: A theory of the brain: localist representation is used widely in the brain An extension of the localist representation theory: grandmother cells are also widely used in the brain Asim Roy Arizona State University http://lifeboat.com/ex/bios.asim.roy From: Connectionists [mailto:connectionists-bounces at mailman.srv.cs.cmu.edu] On Behalf Of Brian J Mingus Sent: Thursday, March 13, 2014 6:41 PM To: Juyang Weng Cc: connectionists at mailman.srv.cs.cmu.edu Subject: [SPAM]Re: Connectionists: how the brain works? Hi John, Theories of the brain will come in at multiple levels of abstraction. A reasonable first pass is to take object recognition as a given. It's clear that language and general intelligence doesn't require it. Hellen Keller is a great example - deaf and blind, and with patience, extremely intelligent. Visual and auditory object recognition simply aren't required! Brian On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng > wrote: Danko, Good attempt. Any theory about brain/mind must address the First Principle: How it learns visual invariance directly from natural cluttered environments. Your article does not seem to address the First Principle, does it? -John On 3/7/14 11:22 AM, Danko Nikolic wrote: I believe that the readers of Connectionists list my be interested in the manuscript available on arXiv (1402.5332) proposing the principles by which adaptive systems create intelligent behavior. It is a theoretical paper that has been recently submitted to a journal, and the editors agreed to post it on arXiv. A nice context for this manuscript is, I think, the recent discussion on Connectionists list on "how the brain works?", -- including the comparison to how the radio works, arguments that neuroscience has not reached the maturity of 19th century physics, that the development should be an essential component, etc. I assess that anyone who enjoyed following that discussion, like I did, would be interested also in what the proposed theory has to say. The theory addresses those problems by placing the question of brain workings one level more abstract than it is usually discussed: It proposes a general set of properties that adaptive systems need to have to exhibit intelligent behavior (nevertheless, concrete examples are given from biology and technology). Finally, the theory proposes what is, in principle, missing in the current approaches in order to account for the higher, biological-like levels of adaptive behavior. For those who are interested, I recommend using the link on my website: http://www.danko-nikolic.com/practopoiesis/ because there I provided, in addition, a simplified introduction into some of the main conclusions derived from the theory. I would very much like to know what people think. Comments will be appreciated. With warm greetings from Germany, Danko Nikolic -- -- Juyang (John) Weng, Professor Department of Computer Science and Engineering MSU Cognitive Science Program and MSU Neuroscience Program 428 S Shaw Ln Rm 3115 Michigan State University East Lansing, MI 48824 USA Tel: 517-353-4388 Fax: 517-432-1061 Email: weng at cse.msu.edu URL: http://www.cse.msu.edu/~weng/ ---------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From bower at uthscsa.edu Fri Mar 14 17:29:58 2014 From: bower at uthscsa.edu (james bower) Date: Fri, 14 Mar 2014 16:29:58 -0500 Subject: Connectionists: how the brain works? In-Reply-To: <532347AA.2020004@cse.msu.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> Message-ID: <1B79B4C4-59DA-410B-8AFE-48EB92CE4049@uthscsa.edu> I would caution again that the brain might know much more about the structure of the world at birth than we know. That makes the traditional feedforward approach to object recognition (and learning) suspect. IMHO Jim bower On Mar 14, 2014, at 1:17 PM, Juyang Weng wrote: > > It's clear that language and general intelligence doesn't require it. > > This is clearly wrong if you know and understand our DN. I believe that any brain theory will miss the boat if it cannot explain the First Principle. The brain is not just an information processor, it is first a developer for the information processor. If one does not understand how the information processor develops, he definitely misses the boat in explaining how the brain processes information. > > That is why although "theories of the brain will come in at multiple levels of abstraction", they may miss the boat. > The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). > > -John > > On 3/13/14 9:40 PM, Brian J Mingus wrote: >> Hi John, >> >> Theories of the brain will come in at multiple levels of abstraction. A reasonable first pass is to take object recognition as a given. It's clear that language and general intelligence doesn't require it. Hellen Keller is a great example - deaf and blind, and with patience, extremely intelligent. Visual and auditory object recognition simply aren't required! >> >> Brian >> >> >> >> >> On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng wrote: >> Danko, >> >> Good attempt. >> >> Any theory about brain/mind must address the First Principle: How it learns visual invariance directly from natural cluttered environments. >> Your article does not seem to address the First Principle, does it? >> >> -John >> >> >> On 3/7/14 11:22 AM, Danko Nikolic wrote: >> I believe that the readers of Connectionists list my be interested in the manuscript available on arXiv (1402.5332) proposing the principles by which adaptive systems create intelligent behavior. It is a theoretical paper that has been recently submitted to a journal, and the editors agreed to post it on arXiv. >> >> A nice context for this manuscript is, I think, the recent discussion on Connectionists list on "how the brain works?", -- including the comparison to how the radio works, arguments that neuroscience has not reached the maturity of 19th century physics, that the development should be an essential component, etc. >> >> I assess that anyone who enjoyed following that discussion, like I did, would be interested also in what the proposed theory has to say. >> >> The theory addresses those problems by placing the question of brain workings one level more abstract than it is usually discussed: It proposes a general set of properties that adaptive systems need to have to exhibit intelligent behavior (nevertheless, concrete examples are given from biology and technology). Finally, the theory proposes what is, in principle, missing in the current approaches in order to account for the higher, biological-like levels of adaptive behavior. >> >> For those who are interested, I recommend using the link on my website: >> >> http://www.danko-nikolic.com/practopoiesis/ >> >> because there I provided, in addition, a simplified introduction into some of the main conclusions derived from the theory. >> >> I would very much like to know what people think. Comments will be appreciated. >> >> With warm greetings from Germany, >> >> Danko Nikolic >> >> >> -- >> -- >> Juyang (John) Weng, Professor >> Department of Computer Science and Engineering >> MSU Cognitive Science Program and MSU Neuroscience Program >> 428 S Shaw Ln Rm 3115 >> Michigan State University >> East Lansing, MI 48824 USA >> Tel: 517-353-4388 >> Fax: 517-432-1061 >> Email: weng at cse.msu.edu >> URL: http://www.cse.msu.edu/~weng/ >> ---------------------------------------------- >> >> > > -- > -- > Juyang (John) Weng, Professor > Department of Computer Science and Engineering > MSU Cognitive Science Program and MSU Neuroscience Program > 428 S Shaw Ln Rm 3115 > Michigan State University > East Lansing, MI 48824 USA > Tel: 517-353-4388 > Fax: 517-432-1061 > Email: weng at cse.msu.edu > URL: http://www.cse.msu.edu/~weng/ > ---------------------------------------------- > -------------- next part -------------- An HTML attachment was scrubbed... URL: From achler at gmail.com Fri Mar 14 18:01:14 2014 From: achler at gmail.com (Tsvi Achler) Date: Fri, 14 Mar 2014 15:01:14 -0700 Subject: Connectionists: how the brain works? In-Reply-To: <1B79B4C4-59DA-410B-8AFE-48EB92CE4049@uthscsa.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <1B79B4C4-59DA-410B-8AFE-48EB92CE4049@uthscsa.edu> Message-ID: Jim, I can't agree with you more that the traditional feedforward approach to object recognition is suspect, but unfortunately academia has an addiction to this. My experience is that academia would rather bury someone that suggests a different computational model than truly evaluate it. -Tsvi On Fri, Mar 14, 2014 at 2:29 PM, james bower wrote: > I would caution again that the brain might know much more about the > structure of the world at birth than we know. > > That makes the traditional feedforward approach to object recognition (and > learning) suspect. > > IMHO > > Jim bower > > > On Mar 14, 2014, at 1:17 PM, Juyang Weng wrote: > >> It's clear that language and general intelligence doesn't require it. > > This is clearly wrong if you know and understand our DN. I believe that any > brain theory will miss the boat if it cannot explain the First Principle. > The brain is not just an information processor, it is first a developer for > the information processor. If one does not understand how the information > processor develops, he definitely misses the boat in explaining how the > brain processes information. > > That is why although "theories of the brain will come in at multiple levels > of abstraction", they may miss the boat. > The brain uses a single architecture to do all brain functions we are aware > of! It uses the same architecture to do vision, audition, motor, reasoning, > decision making, motivation (including pain avoidance and pleasure seeking, > novelty seeking, higher emotion, etc.). > > -John > > On 3/13/14 9:40 PM, Brian J Mingus wrote: > > Hi John, > > Theories of the brain will come in at multiple levels of abstraction. A > reasonable first pass is to take object recognition as a given. It's clear > that language and general intelligence doesn't require it. Hellen Keller is > a great example - deaf and blind, and with patience, extremely intelligent. > Visual and auditory object recognition simply aren't required! > > Brian > > > > > On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng wrote: >> >> Danko, >> >> Good attempt. >> >> Any theory about brain/mind must address the First Principle: How it >> learns visual invariance directly from natural cluttered environments. >> Your article does not seem to address the First Principle, does it? >> >> -John >> >> >> On 3/7/14 11:22 AM, Danko Nikolic wrote: >>> >>> I believe that the readers of Connectionists list my be interested in the >>> manuscript available on arXiv (1402.5332) proposing the principles by which >>> adaptive systems create intelligent behavior. It is a theoretical paper that >>> has been recently submitted to a journal, and the editors agreed to post it >>> on arXiv. >>> >>> A nice context for this manuscript is, I think, the recent discussion on >>> Connectionists list on "how the brain works?", -- including the comparison >>> to how the radio works, arguments that neuroscience has not reached the >>> maturity of 19th century physics, that the development should be an >>> essential component, etc. >>> >>> I assess that anyone who enjoyed following that discussion, like I did, >>> would be interested also in what the proposed theory has to say. >>> >>> The theory addresses those problems by placing the question of brain >>> workings one level more abstract than it is usually discussed: It proposes a >>> general set of properties that adaptive systems need to have to exhibit >>> intelligent behavior (nevertheless, concrete examples are given from biology >>> and technology). Finally, the theory proposes what is, in principle, missing >>> in the current approaches in order to account for the higher, >>> biological-like levels of adaptive behavior. >>> >>> For those who are interested, I recommend using the link on my website: >>> >>> http://www.danko-nikolic.com/practopoiesis/ >>> >>> because there I provided, in addition, a simplified introduction into >>> some of the main conclusions derived from the theory. >>> >>> I would very much like to know what people think. Comments will be >>> appreciated. >>> >>> With warm greetings from Germany, >>> >>> Danko Nikolic >>> >> >> -- >> -- >> Juyang (John) Weng, Professor >> Department of Computer Science and Engineering >> MSU Cognitive Science Program and MSU Neuroscience Program >> 428 S Shaw Ln Rm 3115 >> Michigan State University >> East Lansing, MI 48824 USA >> Tel: 517-353-4388 >> Fax: 517-432-1061 >> Email: weng at cse.msu.edu >> URL: http://www.cse.msu.edu/~weng/ >> ---------------------------------------------- >> > > > -- > -- > Juyang (John) Weng, Professor > Department of Computer Science and Engineering > MSU Cognitive Science Program and MSU Neuroscience Program > 428 S Shaw Ln Rm 3115 > Michigan State University > East Lansing, MI 48824 USA > Tel: 517-353-4388 > Fax: 517-432-1061 > Email: weng at cse.msu.edu > URL: http://www.cse.msu.edu/~weng/ > ---------------------------------------------- > > From bower at uthscsa.edu Fri Mar 14 22:57:06 2014 From: bower at uthscsa.edu (james bower) Date: Fri, 14 Mar 2014 21:57:06 -0500 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <1B79B4C4-59DA-410B-8AFE-48EB92CE4049@uthscsa.edu> Message-ID: In the end, the brain will win - it will just take a while (took 1500 years for the solar system) :-) Jim On Mar 14, 2014, at 5:01 PM, Tsvi Achler wrote: > Jim, > I can't agree with you more that the traditional feedforward approach > to object recognition is suspect, but unfortunately academia has an > addiction to this. > My experience is that academia would rather bury someone that suggests > a different computational model than truly evaluate it. > -Tsvi > > On Fri, Mar 14, 2014 at 2:29 PM, james bower wrote: >> I would caution again that the brain might know much more about the >> structure of the world at birth than we know. >> >> That makes the traditional feedforward approach to object recognition (and >> learning) suspect. >> >> IMHO >> >> Jim bower >> >> >> On Mar 14, 2014, at 1:17 PM, Juyang Weng wrote: >> >>> It's clear that language and general intelligence doesn't require it. >> >> This is clearly wrong if you know and understand our DN. I believe that any >> brain theory will miss the boat if it cannot explain the First Principle. >> The brain is not just an information processor, it is first a developer for >> the information processor. If one does not understand how the information >> processor develops, he definitely misses the boat in explaining how the >> brain processes information. >> >> That is why although "theories of the brain will come in at multiple levels >> of abstraction", they may miss the boat. >> The brain uses a single architecture to do all brain functions we are aware >> of! It uses the same architecture to do vision, audition, motor, reasoning, >> decision making, motivation (including pain avoidance and pleasure seeking, >> novelty seeking, higher emotion, etc.). >> >> -John >> >> On 3/13/14 9:40 PM, Brian J Mingus wrote: >> >> Hi John, >> >> Theories of the brain will come in at multiple levels of abstraction. A >> reasonable first pass is to take object recognition as a given. It's clear >> that language and general intelligence doesn't require it. Hellen Keller is >> a great example - deaf and blind, and with patience, extremely intelligent. >> Visual and auditory object recognition simply aren't required! >> >> Brian >> >> >> >> >> On Thu, Mar 13, 2014 at 6:38 PM, Juyang Weng wrote: >>> >>> Danko, >>> >>> Good attempt. >>> >>> Any theory about brain/mind must address the First Principle: How it >>> learns visual invariance directly from natural cluttered environments. >>> Your article does not seem to address the First Principle, does it? >>> >>> -John >>> >>> >>> On 3/7/14 11:22 AM, Danko Nikolic wrote: >>>> >>>> I believe that the readers of Connectionists list my be interested in the >>>> manuscript available on arXiv (1402.5332) proposing the principles by which >>>> adaptive systems create intelligent behavior. It is a theoretical paper that >>>> has been recently submitted to a journal, and the editors agreed to post it >>>> on arXiv. >>>> >>>> A nice context for this manuscript is, I think, the recent discussion on >>>> Connectionists list on "how the brain works?", -- including the comparison >>>> to how the radio works, arguments that neuroscience has not reached the >>>> maturity of 19th century physics, that the development should be an >>>> essential component, etc. >>>> >>>> I assess that anyone who enjoyed following that discussion, like I did, >>>> would be interested also in what the proposed theory has to say. >>>> >>>> The theory addresses those problems by placing the question of brain >>>> workings one level more abstract than it is usually discussed: It proposes a >>>> general set of properties that adaptive systems need to have to exhibit >>>> intelligent behavior (nevertheless, concrete examples are given from biology >>>> and technology). Finally, the theory proposes what is, in principle, missing >>>> in the current approaches in order to account for the higher, >>>> biological-like levels of adaptive behavior. >>>> >>>> For those who are interested, I recommend using the link on my website: >>>> >>>> http://www.danko-nikolic.com/practopoiesis/ >>>> >>>> because there I provided, in addition, a simplified introduction into >>>> some of the main conclusions derived from the theory. >>>> >>>> I would very much like to know what people think. Comments will be >>>> appreciated. >>>> >>>> With warm greetings from Germany, >>>> >>>> Danko Nikolic >>>> >>> >>> -- >>> -- >>> Juyang (John) Weng, Professor >>> Department of Computer Science and Engineering >>> MSU Cognitive Science Program and MSU Neuroscience Program >>> 428 S Shaw Ln Rm 3115 >>> Michigan State University >>> East Lansing, MI 48824 USA >>> Tel: 517-353-4388 >>> Fax: 517-432-1061 >>> Email: weng at cse.msu.edu >>> URL: http://www.cse.msu.edu/~weng/ >>> ---------------------------------------------- >>> >> >> >> -- >> -- >> Juyang (John) Weng, Professor >> Department of Computer Science and Engineering >> MSU Cognitive Science Program and MSU Neuroscience Program >> 428 S Shaw Ln Rm 3115 >> Michigan State University >> East Lansing, MI 48824 USA >> Tel: 517-353-4388 >> Fax: 517-432-1061 >> Email: weng at cse.msu.edu >> URL: http://www.cse.msu.edu/~weng/ >> ---------------------------------------------- >> >> From ahu at cs.stir.ac.uk Sat Mar 15 17:50:16 2014 From: ahu at cs.stir.ac.uk (Dr Amir Hussain) Date: Sat, 15 Mar 2014 21:50:16 +0000 Subject: Connectionists: Cognitive Computation journal (Springer): Table of Contents, Vol.6, No.1 / Mar 2014 Issue Message-ID: Dear Colleagues: (with advance apologies for any cross-postings) We are delighted to announce the publication of Volume 6, No.1 / March 2014 Issue, of Springer's Cognitive Computation journal - www.springer.com/12559 The individual list of published articles (Table of Contents) for this Issue can be viewed here (and also at the end of this message, followed by an overview of the previous Issues/Archive listings): http://link.springer.com/journal/12559/6/1/ You may also be interested in the journal's Seminal Special Issue (Sep 2013 Issue): In Memory of John G Taylor: A Polymath Scholar, by Guest Editors: Vassilis Cutsuridis and Amir Hussain (the Guest Editorial is available here: http://link.springer.com/content/pdf/10.1007%2Fs12559-013-9226-z.pdf and full listing of articles can be found at: http://link.springer.com/journal/12559/5/3/page/1) A list of the journal's most downloaded articles (which can always be read for FREE) can be found here: http://www.springer.com/biomed/neuroscience/journal/12559?hideChart=1#realtime Other 'Online First' published articles not yet in a print issue can be viewed here: http://www.springerlink.com/content/121361/?Content+Status=Accepted All previous Volumes and Issues of the journal can be viewed here: http://link.springer.com/journal/volumesAndIssues/12559 ======================================================= NEW: ISI Impact Factor for Cognitive Computation of 0.867 for 2012 (5 year IF: 1.137) ======================================================= As you will know, Cognitive Computation was selected for coverage in Thomson Reuter?s products and services in 2011. Beginning with V.1 (1) 2009, this publication is now indexed and abstracted in: ? Science Citation Index Expanded (also known as SciSearch?) ? Journal Citation Reports/Science Edition ? Current Contents?/Engineering Computing and Technology ? Neuroscience Citation Index? Cognitive Computation also received its first Impact Factor of 1.0 (Thomson Reuters Journal Citation Reports? 2011) in 2011 0.867(Thomson Reuters Journal Citation Reports? 2011) in 2012 ============================================ Reminder: New Cognitive Computation "LinkedIn" Group: ============================================ To further strengthen the bonds amongst the interdisciplinary audience of Cognitive Computation, we have set-up a "Cognitive Computation LinkedIn group", which has over 700 members already! We warmly invite you to join us at: http://www.linkedin.com/groups?gid=3155048 For further information on the journal and to sign up for electronic "Table of Contents alerts" please visit the Cognitive Computation homepage: http://www.springer.com/12559 or follow us on Twitter at: http://twitter.com/CognComput for the latest On-line First Issues. For any questions with regards to LinkedIn and/or Twitter, please contact Springer's Publishing Editor: Dr. Martijn Roelandse: martijn.roelandse at springer.com Finally, we would like to invite you to submit short or regular papers describing original research or timely review of important areas - our aim is to peer review all papers within approximately six-eight weeks of receipt. We also welcome relevant high quality proposals for Special Issues - five are already planned for 2014-15 (for CFPs, see: http://www.springer.com/biomed/neuroscience/journal/12559?detailsPage=press ) With our very best wishes to all aspiring readers and authors of Cognitive Computation, Professor Amir Hussain, PhD (Editor-in-Chief: Cognitive Computation) E-mail: ahu at cs.stir.ac.uk (University of Stirling, Scotland, UK) Professor Igor Aleksander, PhD (Honorary Editor-in-Chief: Cognitive Computation) (Imperial College, London, UK) --------------------------------------------------------------------------------------------------------------- Table of Contents Alert -- Cognitive Computation Vol 6 No 1, Mar 2014 --------------------------------------------------------------------------------------------------------------- A Computational Cognitive Model of Information Search in Textual Materials Myriam Chanceaux , Anne Gu?rin-Dugu? , Beno?t Lemaire & Thierry Baccino http://link.springer.com/article/10.1007/s12559-012-9200-1 Artificial Development of Biologically Plausible Neural-Symbolic Networks Joe Townsend , Ed Keedwell & Antony Galton http://link.springer.com/article/10.1007/s12559-013-9217-0 BrainSpace: Relating Neuroscience to Knowledge About Everyday Life Newton Howard & Henry Lieberman http://link.springer.com/article/10.1007/s12559-012-9171-2 Taxonomical Associative Memory Diogo Rendeiro , Jo?o Sacramento & Andreas Wichert http://link.springer.com/article/10.1007/s12559-012-9198-4 Searching the Hyper-heuristic Design Space Jerry Swan , John Woodward , Ender ?zcan , Graham Kendall & Edmund Burke http://link.springer.com/article/10.1007/s12559-013-9201-8 Analogical Mapping with Sparse Distributed Memory: A Simple Model that Learns to Generalize from Examples Blerim Emruli & Fredrik Sandin http://link.springer.com/article/10.1007/s12559-013-9206-3 Characteristic Analysis of Bioinspired Pod Structure Robotic Configurations Azfar Khalid , Samir Mekid & Aamir Hussain http://link.springer.com/article/10.1007/s12559-013-9210-7 A Multimodal Connectionist Architecture for Unsupervised Grounding of Spatial Language Michal Vavre?ka & Igor Farka? http://link.springer.com/article/10.1007/s12559-013-9212-5 Toward a Formal, Visual Framework of Emergent Cognitive Development of Scholars Amir Hussain & Muaz Niazi http://link.springer.com/article/10.1007/s12559-013-9219-y Region-Based Artificial Visual Attention in Space and Time Jan T?nnermann & B?rbel Mertsching http://link.springer.com/article/10.1007/s12559-013-9220-5 --------------------------------------------------- Previous Issues/Archive: Overview: --------------------------------------------------- All previous Volumes and Issues can be viewed here: http://link.springer.com/journal/volumesAndIssues/12559 Alternatively, the full listing of the Inaugural Vol. 1, No. 1 / March 2009, can be viewed here (which included invited authoritative reviews by leading researchers in their areas - including keynote papers from London University's John Taylor, Igor Aleksander and Stanford University's James McClelland, and invited papers from Ron Sun, Pentti Haikonen, Geoff Underwood, Kevin Gurney, Claudius Gross, Anil Seth and Tom Ziemke): http://www.springerlink.com/content/1866-9956/1/1/ The full listing of Vol. 1, No. 2 / June 2009, can be viewed here (which included invited reviews and original research contributions from leading researchers, including Rodney Douglas, Giacomo Indiveri, Jurgen Schmidhuber, Thomas Wennekers, Pentti Kanerva and Friedemann Pulvermuller): http://www.springerlink.com/content/1866-9956/1/2/ The full listing of Vol.1, No. 3 / Sep 2009, can be viewed here: http://www.springerlink.com/content/1866-9956/1/3/ The full listing of Vol. 1, No. 4 / Dec 2009, can be viewed here: http://www.springerlink.com/content/1866-9956/1/4/ The full listing of Vol.2, No. 1 / March 2010, can be viewed here: http://www.springerlink.com/content/1866-9956/2/1/ The full listing of Vol.2, No. 2 / June 2010, can be viewed here: http://www.springerlink.com/content/1866-9956/2/2/ The full listing of Vol.2, No. 3 / Aug 2010, can be viewed here: http://www.springerlink.com/content/1866-9956/2/3/ The full listing of Vol.2, No. 4 / Dec 2010, can be viewed here: http://www.springerlink.com/content/1866-9956/2/4/ The full listing of Vol.3, No.1 / Mar 2011 (Special Issue on: Saliency, Attention, Active Visual Search and Picture Scanning, edited by John Taylor and Vassilis Cutsuridis), can be viewed here: http://www.springerlink.com/content/1866-9956/3/1/ The Guest Editorial can be viewed here: http://www.springerlink.com/content/hu2245056415633l/ The full listing of Vol.3, No.2 / June 2011 can be viewed here: http://www.springerlink.com/content/1866-9956/3/2/ The full listing of Vol. 3, No. 3 / Sep 2011 (Special Issue on: Cognitive Behavioural Systems, Guest Edited by: Anna Esposito, Alessandro Vinciarelli, Simon Haykin, Amir Hussain and Marcos Faundez-Zanuy), can be viewed here: http://www.springerlink.com/content/1866-9956/3/3/ The Guest Editorial for the special issue can be viewed here: http://www.springerlink.com/content/h4718567520t2h84/ The full listing of Vol. 3, No. 4 / Dec 2011 can be viewed here: http://www.springerlink.com/content/1866-9956/3/4/ The full listing of Vol. 4, No.1 / Mar 2012 can be viewed here: http://www.springerlink.com/content/1866-9956/4/1/ The full listing of Vol. 4, No.2 / June 2012 can be viewed here: http://www.springerlink.com/content/1866-9956/4/2/ The full listing of Vol. 4, No.3 / Sep 2012 (Special Issue on: Computational Creativity, Intelligence and Autonomy, Edited by: J. Mark Bishop and Yasemin J. Erden) can be viewed here: http://www.springerlink.com/content/1866-9956/4/3/ The full listing of Vol. 4, No.4 / Dec 2012 (Special Issue titled: "Cognitive & Emotional Information Processing", Edited by: Stefano Squartini, Bj?rn Schuller and Amir Hussain, which is followed by a number of regular papers), can be viewed here: http://link.springer.com/journal/12559/4/4/page/1 The full listing of Vol. 5, No.1 / March 2013 Special Issue titled: Computational Intelligence and Applications Guest Editors: Zhigang Zeng & Haibo He, which is followed by a number of regular papers), can be viewed here: http://link.springer.com/journal/12559/5/1/page/1 The full listing of Vol. 5, No.2 / June 2013 Special Issue titled: Advances on Brain Inspired Computing, Guest Editors: Stefano Squartini, Sanqing Hu & Qingshan Liu, which is followed by a number of regular papers), can be viewed here: http://link.springer.com/journal/12559/5/2/page/1 The full listing of Vol. 5, No.3 / Sep 2013 Special Issue titled: In Memory of John G Taylor: A Polymath Scholar, Guest Editors: Vassilis Cutsuridis & Amir Hussain, which is followed by a number of regular papers), can be viewed here: http://link.springer.com/journal/12559/5/3/page/1 The full listing of Vol. 5, No.4 / Dec 2013, which includes regular papers (including an invited paper by Professor Ron Sun, Rensselaer Polytechnic Institute, USA, titled: Moral Judgment, Human Motivation, and Neural Networks), and a Special Issue titled: Advanced Cognitive Systems Based on Nonlinear Analysis. Guest Editors: Carlos M. Travieso and Jes?s B. Alonso, can be viewed here: http://link.springer.com/journal/12559/5/4/page/1 -------------------------------------------------------------------------------------------- The University of Stirling is ranked in the top 50 in the world in The Times Higher Education 100 Under 50 table, which ranks the world's best 100 universities under 50 years old. The University of Stirling is a charity registered in Scotland, number SC 011159. -- The University of Stirling has been ranked in the top 12 of UK universities for graduate employment*. 94% of our 2012 graduates were in work and/or further study within six months of graduation. *The Telegraph The University of Stirling is a charity registered in Scotland, number SC 011159. From n.lepora at sheffield.ac.uk Sun Mar 16 06:00:57 2014 From: n.lepora at sheffield.ac.uk (Nathan F Lepora) Date: Sun, 16 Mar 2014 10:00:57 +0000 Subject: Connectionists: [meetings] Living Machines III: Extended deadline March 28th for paper submission Message-ID: ______________________________________________________________ Deadline extension for Paper submission Living Machines III: The 3rd International Conference on Biomimetic and Biohybrid Systems 30th July to 1st August 2014 http://csnetwork.eu/livingmachines To be hosted at the Museo Nazionale Della Scienza E Della Tecnologia Leonardo Da Vinci (National Museum of Science and Technology Leonardo da Vinci) Milan, Italy In association with the Istituto Italiano di Technologia (IIT) Accepted papers will be published in Springer Lecture Notes in Artificial Intelligence Submission deadline now March 28th, 2014 (previously March 14th). Papers should be submitted through the Springer web-portal http://senldogo0039.springer-sbm.com/ocs/conference/submitpaperto/LM2014 ______________________________________________________________ ABOUT LIVING MACHINES 2014 The development of future real-world technologies will depend strongly on our understanding and harnessing of the principles underlying living systems and the flow of communication signals between living and artificial systems. Biomimetics is the development of novel technologies through the distillation of principles from the study of biological systems. The investigation of biomimetic systems can serve two complementary goals. First, a suitably designed and configured biomimetic artefact can be used to test theories about the natural system of interest. Second, biomimetic technologies can provide useful, elegant and efficient solutions to unsolved challenges in science and engineering. Biohybrid systems are formed by combining at least one biological component--an existing living system--and at least one artificial, newly-engineered component. By passing information in one or both directions, such a system forms a new hybrid bio-artificial entity. The following are some examples: * Biomimetic robots and their component technologies (sensors, actuators, processors) that can intelligently interact with their environments. * Active biomimetic materials and structures that self-organize and self-repair. * Biomimetic computers--neuromimetic emulations of the physiological basis for intelligent behaviour. * Biohybrid brain-machine interfaces and neural implants. * Artificial organs and body-parts including sensory organ-chip hybrids and intelligent prostheses. * Organism-level biohybrids such as robot-animal or robot-human systems. ACTIVITIES The main conference will take the form of a three-day single-track oral and poster presentation programme, 30th July to 1st August 2014, hosted at the Museo Nazionale Della Scienza E Della Tecnologia Leonardo Da Vinci in Milan (http://www.museoscienza.org). The conference programme will include five plenary lectures from leading international researchers in biomimetic and biohybrid systems, and the demonstrations of state-of-the-art living machine technologies. Agreed speakers are: Sarah Begreiter, University of Maryland (Microfabrication and robotics) Darwin Caldwell, Italian Institute of Technology (Legged locomotion) Andrew Schwartz, University of Minnesota, Pittsburgh (Neural control of prosthetics) Ricard Sole, Universitat Pompeu Fabra, Barcelona (Self-organization and synthetic biology) Srini Srinivasan, Queensland Brain Institute (Insect-inspired cognition and vision) There will also be a special session on biomimetics in design, including a talk by Franco Lodato, author of the book 'Bionics in Action.' The full conference will be preceded by up to two days of Satelite Events hosted by the Istituto Italiano di Technologia in Milan. SUBMITTING TO LIVING MACHINES 2014 We invite both full papers and extended abstracts in areas related to the conference themes. All contributions will be refereed and accepted papers will appear in the Living Machines 2014 proceedings published in the Springer-Verlag LNAI Series. Submissions should be made before the advertised deadline via the Springer submission site:http://senldogo0039.springer-sbm.com/ocs/en/home/LM2014 Full papers (up to 12 pages) are invited from researchers at any stage in their career but should present significant findings and advances in biomimetic or biohybid research; more preliminary work would be better suited to extended abstract submission (3 pages). Further details of submission formats will be circulated in an updated CfP and will be posted on the conference web-site. Full papers will be accepted for either oral presentation (single track) or poster presentation. Extended abstracts will be accepted for poster presentation only. Authors of the best full papers will be invited to submitted extended versions of their paper for publication in a special issue of Bioinspiration and Biomimetics. Satellite events Active researchers in biomimetic and biohybrid systems are invited to propose topics for 1-day or 2-day tutorials, symposia or workshops on related themes to be held 28-29th July at Italian Institute of Technology in Milan. Events can be scheduled on either the 28th or 29th or across both days. Attendance at satellite events will attract a small fee intended to cover the costs of the meeting. There is a lot of flexibility about the content, organisation, and budgeting for these events. Please contact us if you are interested in organising a satellite event! EXPECTED DEADLINES March 28th, 2014 Paper submission deadline (previously March 14th) April 29th, 2014 Notification of acceptance May 20th, 2014 Camera ready copy July 29-August 2nd 2014 Conference SPONSORSHIP Living Machines 2014 is sponsored by the Convergent Science Network (CSN) for Biomimetics and Neurotechnology. CSN is an EU FP7 Future Emerging Technologies Co-ordination Activity that also organises many highly successful workshop series: the Barcelona Summer School on Brain, Technology and Cognition, the Capo Caccia Neuromorphic Cognitive Engineering Workshop, the School on Neuro-techniques, the Okinawa School of Computational Neuroscience and the Telluride workshop of Cognitive Neuromorphic Engineering (see http://csnetwork.eu/activities for details) The 2014 Living Machines conference will also be hosted and sponsored by the Istituto Italiano di Technologia (http://www.iit.it). Call for Sponsors. Other organisations wishing to sponsor the conference in any way and gain the corresponding benefits by promoting themselves and their products to through conference publications, the conference web-site, and conference publicity are encouraged to contact the conference organisers to discuss the terms of sponsorship and necessary arrangements. We offer a number of attractive and good-value packages to potential sponsors. ABOUT THE VENUE Living Machines 2014 continues our practice of hosting our annual meeting in an inspirational venue related to the conference themes. The scientific and technological genius Leonardo da Vinci drew much of his inspiration from biology and invented many biomimetic artefacts. We are therefore delighted that this year's conference will be hosted at the Da Vinci museum of Science and Technology in Milan, one of the largest technology museums in Europe and host to a collection of working machines that realise many of Da Vinci's ideas. We look forward to seeing you in Milan. Organising Committee: Tony Prescott, University of Sheffield (Co-chair) Paul Verschure, Universitat Pompeu Fabra (Co-chair) Armin Duff, Universitat Pompeu Fabra (Program Chair) Giorgio Metta, Instituto Italiano di Technologia (Local Organizer) Barbara Mazzolai, Instituto Italiano di Technologia (Local Organiser) Anna Mura, Universitat Pompeu Fabra (Communications) Nathan Lepora, University of Bristol (Communications) Program Committee: Anders Lyhne Christensen Andy Adamatzky Andy Phillipides Arianna Menciassi Auke Ijspeert Barry Trimmer Ben Mitchinson Benoit Girard Cecilia Laschi Charles Fox Chrisantha Fernando Christophe Grand Danilo de Rossi Darwin Caldwell Dieter Braun Emre Neftci Enrico Pagello Eris Chinaletto Ferdinando Rodrigues y Baena Frank Grasso Fred Claeyssens Frederic Boyer Frederico Carpi Giacomo Indiveri Gregory Chirikjian Hillel Chiel Holger Krapp Holk Cruse Husosheng Hu Jess Krichmar Jira Okada John Hallam Jon Timmis Jonathan Rossiter Jose Halloy Joseph Ayers Julian Vincent Keisuke Morisima Lucia Beccai Marco Dorigo Mark Cutkosky Martin Pearson Mat Evans Mehdi Khamassi Michele Giogliano Nathan Lepora Noah Cowan Pablo Varona Paul Graham Paul Verschure Reiko Tanaka Robert Allen Roberto Cingolani Roderich Gross Roger Quinn Sean Anderson Serge Kernbach Simon Garnier Stephane Doncieux Stuart Wilson Thomas Schmickl Tim Pearce Tony Pipe Tony Prescott Volker Durr Wolfgang Eberle Yiannis Demiris Yoseph Bar-Cohen From aconway at fields.utoronto.ca Sat Mar 15 10:18:52 2014 From: aconway at fields.utoronto.ca (Alison Conway) Date: Sat, 15 Mar 2014 10:18:52 -0400 Subject: Connectionists: Announcement-- Postdoctoral Fellowships - Program on Statistical Inference in Big Data Message-ID: Please distribute this posting ------------------------------------------------------------------ Postdoctoral Fellowships - Program on Statistical Inference in Big Data ------------------------------------------------------------------ Applications are invited for postdoctoral fellowship positions with the planned Program on Statistical Inference in Big Data, January to June 2015, at the Fields Institute, Toronto. The fellowships provide for a period of engagement in research and participation in the activities of the Institute. They may be offered in conjunction with partner universities, through which a further period of support may be possible. List on the cover sheet of the application any faculty members, at universities affiliated with the Fields Institute, who you believe are appropriate. You are encouraged to apply directly to these other institutions as well. One of the postdoctoral fellows for the six-month program will be awarded the Institute's prestigious Jerrold E. Marsden Postdoctoral Fellowship. This pays a full stipend and provides for a six-month period at the Institute for research and participation in the activities of the thematic program with no teaching required. In addition to the stipend, a $1,000 (Cdn) research grant will be available during the tenure of the award. There may also be a number of two year positions available connected to the Fields-Ontario fellowship. These fellowships involve a semester at Fields and 3 semesters at one of Fields' Principal Sponsoring Universities (PSU). To apply for the two year fellowship please indicate your interest on the cover sheet and include the names of faculty members at Fields Principal Sponsoring Universities who may be appropriate as supervisors. The Fields PSU's are: Carleton University, McMaster University, the University of Ottawa, the University of Toronto, the University of Waterloo, the University of Western Ontario, and York University. Eligibility: Qualified candidates who will have a recent PhD in the area of the Program or a related area of the statistical, computational or mathematical sciences are encouraged to apply. Deadline to apply is June 1, 2014. Fellowship positions are conditional on funding for the Program. Late applications will also be accepted until the positions are filled. Apply: https://www.mathjobs.org/jobs/jobs/5748 With thanks, Alison Conway ====================================== Alison E. Conway | Manager of Scientific Programs FIELDS INSTITUTE | 222 College St, Toronto, M5T 3J1 ===================================== From kkuehnbe at uos.de Sun Mar 16 19:43:41 2014 From: kkuehnbe at uos.de (Kai-Uwe Kuehnberger) Date: Mon, 17 Mar 2014 00:43:41 +0100 Subject: Connectionists: "Neural-Symbolic Networks for Cognitive Capacities" - 2nd Call for Papers for a special issue of Elsevier's "Biologically Inspired Cognitive Architectures" In-Reply-To: <52F39212.2070902@uos.de> References: <52F39212.2070902@uos.de> Message-ID: <5326372D.9050708@uos.de> 2nd Call for Papers: Journal Special Issue on == Neural-Symbolic Networks for Cognitive Capacities == Tarek R. Besold, Artur d'Avila Garcez, Kai-Uwe K?hnberger, Terrence C. Stewart Special issue of the Elsevier Journal on Biologically Inspired Cognitive Architectures (BICA) http://www.journals.elsevier.com/biologically-inspired-cognitive-architectures/ = SCOPE = Researchers in artificial intelligence and cognitive systems modelling continue to face foundational challenges in their quest to develop plausible models and implementations of cognitive capacities and intelligence. One of the methodological core issues is the question of the integration between sub-symbolic and symbolic approaches to knowledge representation, learning and reasoning in cognitively-inspired models. Network-based approaches very often enable flexible tools which can discover and process the internal structure of (possibly large) data sets. They promise to give rise to efficient signal-processing models which are biologically plausible and optimally suited for a wide range of applications, whilst possibly also offering an explanation of cognitive phenomena of the human brain. Still, the extraction of high-level explicit (i.e. symbolic) knowledge from distributed low-level representations thus far has to be considered a mostly unsolved problem. In recent years, network-based models have seen significant advancement in the wake of the development of the new "deep learning" family of approaches to machine learning. Due to the hierarchically structured nature of the underlying models, these developments have also reinvigorated efforts in overcoming the neural-symbolic divide. The aim of the special issue is to bring together recent work developed in the field of network-based information processing in a cognitive context, which bridges the gap between different levels of description and paradigms and which sheds light onto canonical solutions or principled approaches occurring in the context of neural-symbolic integration to modelling or implementing cognitive capacities. = TOPICS = We particularly encourage submissions related to the following non-exhaustive list of topics: - new learning paradigms of network-based models addressing different knowledge levels - biologically plausible methods and models - integration of network models and symbolic reasoning - cognitive systems using neural-symbolic paradigms - extraction of symbolic knowledge from network-based representations - challenging applications which have the potential to become benchmark problems - visionary papers concerning the future of network approaches to cognitive modelling = SUBMISSIONS = Deadline for submissions is *** April 16, 2014 ***. The suggested submission category for the special issue is Research Article - up to 20 journal pages or 20,000 words -, while shorter submissions in the category Letter - up to 6 journal pages or 5000 words - are equally welcome. Visionary papers dealing with the future of network approaches to cognitive modelling must belong to the category Research Article and are subject to prior acceptance by the editors. If you are planning on submitting to this category, please get in touch with Tarek R. Besold, tbesold at uni-osnabrueck.de. Submissions shall follow the guidelines laid out for the journal "Biologically Inspired Cognitive Architectures", which can be found under http://www.elsevier.com/journals/biologically-inspired-cognitive-architectures/2212-683X/guide-for-authors. Contributions shall be submitted via the journal's submission system which can be found under http://ees.elsevier.com/bica/ and in addition shall be sent by email as .pdf to Tarek R. Besold, tbesold at uni-osnabrueck.de. When submitting their papers online, authors are asked to select "Article Type" SI:Neural-Symbolic Net in order to assure identification of the submission as belonging to the special issue. Please also indicate in the cover letter that the article has been submitted to the special issue on "Neural-Symbolic Networks for Cognitive Capacities". = IMPORTANT DATES = Deadline for submissions: April 16, 2014 Feedback to authors*: May 9, 2014 Submission of revised versions: May 19, 2014 Final notification of acceptance: May 24, 2014 Publication of the special issue: July 2014 as Vol. 9 of "Biologically Inspired Cognitive Architectures" (*= Including rejection / minor revisions / acceptance.) = GUEST EDITORS = Tarek R. Besold, Institute of Cognitive Science, University of Osnabr?ck, Germany Artur D'Avila Garcez, Department of Computer Science, City University London, UK Kai-Uwe K?hnberger, Institute of Cognitive Science, University of Osnabr?ck, Germany Terrence C. Stewart, Centre for Theoretical Neuroscience, University of Waterloo, Canada From rbcp at cin.ufpe.br Mon Mar 17 07:31:36 2014 From: rbcp at cin.ufpe.br (Ricardo Bastos C. Prudencio) Date: Mon, 17 Mar 2014 08:31:36 -0300 Subject: Connectionists: CFP - Brazilian Conference on Intelligent Systems (BRACIS 2014) Message-ID: New deadline for paper submission: April 30th ========================================================= Call for Papers - BRACIS/ENIAC 2014 ========================================================= BRACIS 2014 - Brazilian Conference on Intelligent Systems ENIAC 2014 - Encontro Nacional de Intelig?ncia Artificial e Computacional October 18-23, 2014 - S?o Carlos-SP, Brazil http://jcris2014.icmc.usp.br/index.php/bracis-eniac ========================================================= Submission BRACIS\ENIAC will have a unified submission process. Papers should be written in English or Portuguese. Submitted papers must not exceed 6 pages, including all tables, figures, and references (style of the IEEE Computer Society Press). At maximum, two additional pages are permitted with overlength page charge. Formatting instructions, as well as templates for Word and /LaTeX/, can be found in http://www.computer.org/portal/web/cscps/formatting The best papers in English will be included in the BRACIS proceedings published by IEEE. All accepted papers written in Portuguese will appear in the ENIAC proceedings, published electronically as BDBComp. The remaining accepted papers submitted in English, but not indicated to BRACIS will be suggested for publication in ENIAC. Authors of selected papers will be invited to submit extended versions of their work to be appreciated for publication in special issues of several international journals (to be announced). Papers must be submitted in PDF files through the EasyChair system. https://www.easychair.org/conferences/?conf=braciseniac2014 The deadline for paper registration/upload is April 30th., 23:55 BRST. Submission Policy By submitting papers to BRACIS/ENIAC 2014, the authors agree that in case of acceptance, at least one author should be fully registered to the conference **before** the deadline for sending the camera-ready paper. Accepted papers without the respective author full registration **before** the deadline will not be included in the proceedings. Important dates Deadline Submission for Regular Papers: April 30, 2014 (New deadline) Acceptance notification: June 12, 2014 Final camera-ready papers due: July 7, 2014 Program Chairs: Ricardo Prudencio (UFPE) and Paulo E. Santos (FEI) Local BRACIS Chair: Estevam Rafael Hruschka Junior (UFSCar) and Heloisa de Arruda Camargo (UFSCar) -------------- next part -------------- An HTML attachment was scrubbed... URL: From ctf20 at sussex.ac.uk Mon Mar 17 06:58:53 2014 From: ctf20 at sussex.ac.uk (Dr Chrisantha Fernando) Date: Mon, 17 Mar 2014 03:58:53 -0700 Subject: Connectionists: PhD in Cognitive Robotics (London) Message-ID: <1C040BD0-F7BE-4C12-A658-AF45DE167999@sussex.ac.uk> Dear All, Please could I ask you to kindly advertise my PhD in Cognitive Robotics as widely as possible so that we get the very best candidates applying? Apologies for multiple postings. PhD in Cognitive Robotics Queen Mary, University of London -School of Electronic Engineering and Computer Science http://www.jobs.ac.uk/job/AII997/phd-in-cognitive-robotics/ Best wishes, Dr. Chrisantha Fernando Lecturer in Computer Science Queen Mary University London -------------- next part -------------- An HTML attachment was scrubbed... URL: From irodero at cac.rutgers.edu Mon Mar 17 14:25:06 2014 From: irodero at cac.rutgers.edu (Ivan Rodero) Date: Mon, 17 Mar 2014 14:25:06 -0400 Subject: Connectionists: CAC 2014 - Call for Papers (abstracts due March 31) In-Reply-To: <8569604B-1ABF-4E84-80DF-02604DF42043@rutgers.edu> References: <51EC7783-DCAD-4364-B1DC-576C726BAA31@rutgers.edu> <6F339279-23CD-4553-95DF-B1F906F948E3@rutgers.edu> <0957F75F-5AB9-4144-B62D-87D225B34E42@rutgers.edu> <22CF346C-98EC-4D5B-9500-D0B9FE60551A@rutgers.edu> <99D17D7E-34C0-47B7-B641-C756E67D169A@rutgers.edu> <87202061-93AC-4066-89E7-77976097AFAB@rutgers.edu> <79403393-1690-4DCB-855A-1EE231D5ED2B@rutgers.edu> <5D63B8C8-3FD3-4241-9021-CBD7F84DCAA7@rutgers.edu> <8569604B-1ABF-4E84-80DF-02604DF42043@rutgers.edu> Message-ID: <5F0F4666-E02C-4D89-BD56-596EE179E001@rutgers.edu> CAC 2014 Call for Papers ==================== The International Conference on Cloud and Autonomic Computing (CAC-2014) Part of FAS* - Foundation and Applications of Self* Computing Conferences Collocated with The 8th IEEE Self-Adaptive and Self-Organizing System Conference The 14th IEEE Peer-to-Peer Computing Conference Imperial College, London, September 8-12, 2014 http://www.autonomic-conference.org Important Dates Abstract registration: March 31, 2014 Paper submission due: April 7, 2014 Notification to authors: June 12, 2014 Final paper due: July 12, 2014 Please find attached the complete CFP in PDF format. ============================================================= Ivan Rodero, Ph.D. Rutgers Discovery Informatics Institute (RDI2) NSF Center for Cloud and Autonomic Computing (CAC) Department of Electrical and Computer Engineering Rutgers, The State University of New Jersey Office: CoRE Bldg, Rm 624 94 Brett Road, Piscataway, NJ 08854-8058 Phone: (732) 993-8837 Fax: (732) 445-0593 Email: irodero at rutgers dot edu WWW: http://nsfcac.rutgers.edu/people/irodero ============================================================= -------------- next part -------------- An HTML attachment was scrubbed... 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URL: From erhard.wieser at tum.de Tue Mar 18 06:22:21 2014 From: erhard.wieser at tum.de (Wieser, Erhard) Date: Tue, 18 Mar 2014 10:22:21 +0000 Subject: Connectionists: Call for Papers: ICDL-EPIROB 2014 Message-ID: <65FB3D535850A24A89847846D8264700BEAB28@BADWLRZ-SWMBX11.ads.mwn.de> ======================================================== Call for Papers, Tutorials and Thematic Workshops IEEE ICDL-EPIROB 2014 The Fourth Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics Palazzo Ducale, Genoa, Italy October 13-16, 2014 http://www.icdl-epirob.org/ == Conference description The past decade has seen the emergence of a new scientific field that studies how intelligent biological and artificial systems develop sensorimotor, cognitive and social abilities, over extended periods of time, through dynamic interactions with their physical and social environments. This field lies at the intersection of a number of scientific and engineering disciplines including Neuroscience, Developmental Psychology, Developmental Linguistics, Cognitive Science, Computational Neuroscience, Artificial Intelligence, Machine Learning, and Robotics. Various terms have been associated with this new field such as Autonomous Mental Development, Epigenetic Robotics, Developmental Robotics, etc., and several scientific meetings have been established. The two most prominent conference series of this field, the International Conference on Development and Learning (ICDL) and the International Conference on Epigenetic Robotics (EpiRob), are now joining forces for the fourth time and invite submissions for a joint conference in 2014, to explore and extend the interdisciplinary boundaries of this field. == Keynote speakers Prof. Dana Ballard, Dept. of Computer Sciences, University of Texas at Austin, USA Prof. Cristina Becchio, Dept. of Psychology, University of Turin, ITALY Prof. Tetsur Matsuzawa, Primate Research Institute, Kyoto University, JAPAN == Call for Submissions We invite submissions for this exciting window into the future of developmental sciences. Submissions which establish novel links between brain, behavior and computation are particularly encouraged. == Topics of interest include (but are not limited to): * the development of perceptual, motor, cognitive, emotional, social, and communication skills in biological systems and robots; * embodiment; * general principles of development and learning; * interaction of nature and nurture; * sensitive/critical periods; * developmental stages; * grounding of knowledge and development of representations; * architectures for cognitive development and open-ended learning; * neural plasticity; * statistical learning; * reward and value systems; * intrinsic motivations, exploration and play; * interaction of development and evolution; * use of robots in applied settings such as autism therapy; * epistemological foundations and philosophical issues. Any of the topics above can be simultaneously studied from the neuroscience, psychology or modeling/robotic point of view. == Submissions will be accepted in several formats: 1. Full six-page paper submissions: Accepted papers will be included in the conference proceedings and will be selected for either an oral presentation or a featured poster presentation. Featured posters will have a 1 minute "teaser" presentation as part of the main conference session and will be showcased in the poster sessions. Maximum two-extra pages can be acceptable for a publication fee of 100 euros per page. 2. Two-page poster abstract submissions: To encourage discussion of late-breaking results or for work that is not sufficiently mature for a full paper, we will accept 2-page abstracts. These submissions will NOT be included in the conference proceedings. Accepted abstracts will be presented during poster sessions. 3. Tutorials and workshops: We invite experts in different areas to organize either a tutorial or a workshop to be held on the first day of the conference. Tutorials are meant to provide insights into specific topics as well as overviews that will inform the interdisciplinary audience about the state-of-the-art in child development, neuroscience, robotics, or any of the other disciplines represented at the conference. A workshop is an opportunity to present a topic cumulatively. Workshop can be half- or full-day in duration including oral presentations as well as posters. Submission format: two pages including title, list of speakers, concept and target audience. All submissions will be peer reviewed. Submission website through paperplaza at: http://ras.papercept.net == Important dates April 30th, 2014, paper submission deadline July 15th, 2014, author notification August 31st, 2014, final version (camera ready) due October 13th-16th, 2014, conference == Program committee General Chairs: Giorgio Metta (IIT, Genoa) Mark Lee (Univ. of Aberystwyth) Ian Fasel (Univ. of Arizona) Bridge Chairs: Giulio Sandini (IIT, Genoa) Masako Myowa-Yamakoshi (Univ. Kyoto) Program Chairs: Lorenzo Natale (IIT, Genoa) Erol Sahin (METU, Ankara) Publications Chairs: Francesco Nori (IIT, Genoa) Publicity Chairs: Katrin Lohan (Heriot-Watt, Edinburgh) Gordon Cheng (TUM, Munich) Local chairs: Alessandra Sciutti (IIT, Genoa) Vadim Tikhanoff (IIT, Genoa) Finance chairs: Andrea Derito (IIT, Genoa) -------------- next part -------------- An HTML attachment was scrubbed... URL: From mehdi.khamassi at isir.upmc.fr Tue Mar 18 13:47:01 2014 From: mehdi.khamassi at isir.upmc.fr (Mehdi Khamassi) Date: Tue, 18 Mar 2014 18:47:01 +0100 Subject: Connectionists: Call for posters/registration: Fourth International Symposium on Biology of Decision-Making, 26-28 May 2014 @ Paris, France Message-ID: <41b6fd95fe91a330a4b6a44d4824106d@mailhost.isir.upmc.fr> [Please accept our apologies if you get multiple copies of this message] Dear colleagues, Registration is now open for the Fourth Symposium on Biology of Decision Making which will take place in Paris, France, on May, 26-28th 2014. Online registration is accessible at http://sbdm2014.isir.upmc.fr The deadline for poster submission is on April, 15th. The deadline for registration is on May, 1st. Registration fees (120 euros) include lunches, coffee breaks and access to social events. Please circulate widely and encourage your colleagues to attend. ------------------------------------------------------------------------------------------------ FOURTH SYMPOSIUM ON BIOLOGY OF DECISION MAKING (SBDM 2014) May 26-28, 2014, Paris, France Institut du Cerveau et de la Moelle, H?pital La Piti? Salp?tri?re, Paris, France. & Ecole Normale Sup?rieure, Paris, France. & Universit? Pierre et Marie Curie, Paris, France. http://sbdm2014.isir.upmc.fr ------------------------------------------------------------------------------------------------ PRESENTATION: The Fourth Symposium on Biology of Decision Making will take place on May 26-28, 2014 at the Institut du Cerveau et de la Moelle, Paris, France, with a satellite day at Ecole Normale Sup?rieure, Paris, France. The objective of this three day symposium is to gather people from different research fields with different approaches (economics, ethology, psychiatry, neural and computational approaches) to decision-making. The symposium will be a single-track, will last for 3 days and will include 6 sessions: (#1) Who is making decisions? Cortex or basal ganglia; (#2) New computational approaches to decision-making; (#3) A new player in decision-making: the hippocampus; (#4) Neuromodulation of decision-making; (#5) Maladaptive decisions in clinical conditions; (#6) Who is more rational? Decision making across species. CONFIRMED SPEAKERS: Bernard Balleine (Sydney University, Australia) Karim Benchenane (CNRS-ESPCI, France) Roland Benoit (Harvard, USA) Matthew Botvinick (Princeton University, USA) Anne Collins (Brown University, USA) Roshan Cools (Radboud Univ. Nijmegen, The Netherlands) Molly Crockett (UCL, UK) Jean Daunizeau (INSERM-ICM, France) Nathaniel Daw (NYU, USA) Kenji Doya (OIST, Japan) Philippe Faure (CNRS-UPMC, France) Lesley Fellows (McGill University, Canada) Algo Genovesio (Universita La Sapienza, Italy) Tobias Kalenscher (Universit?t D?sseldorf, Germany) Etienne Koechlin (CNRS-ENS, France) James Marshall (Sheffield University, UK) Genela Morris (Haifa University, Israel) Camilio Padoa-Schioppa (Washington Univ. St Louis, USA) Alex Pouget (Rochester University, USA) Pete Redgrave (Sheffield University, UK) Jonathan Roiser (UCL, UK) Masamichi Sakagami (Tamagawa University, Japan) Daphna Shohamy (Columbia University, USA) Klaas Stephan (Univ. Zurich & ETH Zurich, Switzerland) IMPORTANT DATES: April 15, 2014 Deadline for Poster Submission May 1, 2014 Deadline for Registration May 26-28, 2014 Symposium Venue ORGANIZING COMMITTEE: Thomas Boraud (CNRS, Bordeaux, France) Sacha Bourgeois-Gironde (La Sorbonne, Paris, France) Kenji Doya (OIST, Okinawa, Japan) Mehdi Khamassi (CNRS - UPMC, Paris, France) Etienne Koechlin (CNRS - ENS, Paris, France) Mathias Pessiglione (ICM - INSERM, Paris, France) CONTACT INFORMATION : Website, registration, poster submission and detailed program: http://sbdm2014.isir.upmc.fr Contact: sbdm2014 [ at ] isir.upmc.fr Questions about registration: sbdm2014-registration [ at ] isir.upmc.fr -- Mehdi Khamassi, PhD Researcher (CNRS) Institut des Syst?mes Intelligents et de Robotique (UMR7222) CNRS - Universit? Pierre et Marie Curie Pyramide, Tour 55 - Bo?te courrier 173 4 place Jussieu, 75252 Paris Cedex 05, France tel: + 33 1 44 27 28 85 fax: +33 1 44 27 51 45 cell: +33 6 50 76 44 92 http://people.isir.upmc.fr/khamassi From mark.humphries at manchester.ac.uk Tue Mar 18 13:03:09 2014 From: mark.humphries at manchester.ac.uk (Mark Humphries) Date: Tue, 18 Mar 2014 17:03:09 +0000 Subject: Connectionists: PhD in image analysis for behavioural tracking Message-ID: <7E954275ED82B9468C2C731FB72522F5B667B2AA@MBXP09.ds.man.ac.uk> Posted on behalf of Dr Robyn Grant: Applications are sought for a 3 year, fully-funded PhD position (UK & EU nationals) in the lab of Dr Robyn Grant at Manchester Metropolitan University, UK. Title: "Indicators of Neurodegenerative Disease revealed through Animal Behaviour: open source toolbox development for measurement of disease progression." Summary: A multi-disciplinary project developing image analysis tools for objective, non-invasive assessment of animal behaviour. In particular, the project will focus on developing methods to quantify changes in patterns of locomotion and whisker movements resulting from neurodegenerative disorders such as Motor Neuron Disease, Huntingdon's Disease and ageing. Candidates must have good programming skills (e.g. Matlab) and a strong motivation for research. Expertise of developing image analysis algorithms would be desirable, with an interest in animal behavior. The student will also receive training on image processing techniques and software development, alongside collecting animal behavior data. For more details and to apply: http://www.findaphd.com/search/ProjectDetails.aspx?PJID=53553&LID=1837 For all inquiries, contact Dr Robyn Grant: Robyn.Grant at mmu.ac.uk Dr Mark Humphries MRC Senior non-Clinical Fellow Faculty of Life Sciences AV Hill Building University of Manchester http://www.systemsneurophysiologylab.ls.manchester.ac.uk/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From auke.ijspeert at epfl.ch Tue Mar 18 11:40:03 2014 From: auke.ijspeert at epfl.ch (Auke Ijspeert) Date: Tue, 18 Mar 2014 16:40:03 +0100 Subject: Connectionists: Doctoral Program Robotics, Brain and Cognition (Portugal + EPFL, Switzerland) Message-ID: <532868D3.4010609@epfl.ch> CALL FOR APPLICATIONS FOR DOCTORAL SCHOLARSHIPS The doctoral programme RBCog-PhD ? Robotics, Brain and Cognition ? offers several PhD scholarships in the areas of cognitive robotics, neuroimaging and distributed and socially aware robots. RBCog-PhD focuses on the multidisciplinary use of robotics and neuroimaging with the twin goal of (i) advancing our understanding of brain function in humans and (ii) developing new robotic systems based on biologically plausible principles. The RBCog-PhD is coordinated by Instituto Superior T?cnico (IST-Lisbon) in cooperation with ?cole Polytechnique F?d?rale de Lausanne (EPFL), Universidade de Coimbra (UC), Champalimaud Foundation (CF) and involving the research centres Instituto de Sistemas e Rob?tica Lisbon, INESC-ID and Instituto de Sistemas e Rob?tica in Coimbra. Prospective candidates should hold a M.Sc. degree in Engineering, Computer Science, Physics, Maths, or other related disciplines from reputable universities. Accepted students will be awarded a scholarship and will carry out his/her PhD studies in a multi-national, multi-cultural environment in two institutions of the RBCog-PhD consortium. HOW TO APPLY The call is open between March 14th 2014 and April 6th 2014, at 17:00 GMT. Detailed information on RBCog-PhD: http://rbcog.isr.ist.utl.pt How to apply: http://rbcog.isr.ist.utl.pt/applications.htm AVAILABLE THESIS TOPICS - Visuo motor coordination in manipulation tasks (IST+EPFL) - Vision-based locomotion control (IST+EPFL) - The neural correlates of brain dynamics (IST+EPFL) - Robots and Mutual Understanding: Detecting and Repairing Mis-Understanding in HRI (IST+EPFL) - Vision-based Control of Micro Air Vehicles (IST+EPFL) - Institutional Robotics for Social-Aware Multi-Robot Systems (IST+EPFL) - Bridging neurophysiological and neuroimaging correlates of behavior (IST+CF) - Learning affordances in animals and robots (IST+CF) - Space variant vision in humans and robots (IST+UC) - Vision with non-conventional omnidirectional cameras (IST+UC) - Multi-finger Cooperative Perception (IST+UC) Successful candidates are expected to start by September 2014. Jos? Santos-Victor, IST/ISR RBCog-PhD Program Director P.S> RBCog-PhD is partially funded by the Portuguese Foundation for Science and Technology (FCT). -- =============================================================== Jose' SANTOS-VICTOR Instituto Superior Tecnico jasv at isr.ist.utl.pt Instituto de Sistemas e Robotica www.isr.ist.utl.pt/~jasv Av. Rovisco Pais, 1 +351 21 8418294 (phone) 1049-001 Lisboa, PORTUGAL +351 21 8418291 (fax) ================================================================ From rbcp at cin.ufpe.br Tue Mar 18 14:54:36 2014 From: rbcp at cin.ufpe.br (Ricardo Bastos C. Prudencio) Date: Tue, 18 Mar 2014 15:54:36 -0300 Subject: Connectionists: CFP - Brazilian Conference on Intelligent Systems (BRACIS 2014) Message-ID: New deadline for paper submission: April 30th ========================================================= Call for Papers - BRACIS/ENIAC 2014 ========================================================= BRACIS 2014 - Brazilian Conference on Intelligent Systems ENIAC 2014 - Encontro Nacional de Intelig?ncia Artificial e Computacional October 18-23, 2014 - S?o Carlos-SP, Brazil http://jcris2014.icmc.usp.br/index.php/bracis-eniac ========================================================= Submission BRACIS\ENIAC will have a unified submission process. Papers should be written in English or Portuguese. Submitted papers must not exceed 6 pages, including all tables, figures, and references (style of the IEEE Computer Society Press). At maximum, two additional pages are permitted with overlength page charge. Formatting instructions, as well as templates for Word and /LaTeX/, can be found in http://www.computer.org/portal/web/cscps/formatting The best papers in English will be included in the BRACIS proceedings published by IEEE. All accepted papers written in Portuguese will appear in the ENIAC proceedings, published electronically as BDBComp. The remaining accepted papers submitted in English, but not indicated to BRACIS will be suggested for publication in ENIAC. Authors of selected papers will be invited to submit extended versions of their work to be appreciated for publication in special issues of several international journals (to be announced). Papers must be submitted in PDF files through the EasyChair system. https://www.easychair.org/conferences/?conf=braciseniac2014 The deadline for paper registration/upload is April 30th., 23:55 BRST. Submission Policy By submitting papers to BRACIS/ENIAC 2014, the authors agree that in case of acceptance, at least one author should be fully registered to the conference **before** the deadline for sending the camera-ready paper. Accepted papers without the respective author full registration **before** the deadline will not be included in the proceedings. Important dates Deadline Submission for Regular Papers: April 30, 2014 (New deadline) Acceptance notification: June 12, 2014 Final camera-ready papers due: July 7, 2014 Program Chairs: Ricardo Prudencio (UFPE) and Paulo E. Santos (FEI) Local BRACIS Chair: Estevam Rafael Hruschka Junior (UFSCar) and Heloisa de Arruda Camargo (UFSCar) 2014-03-17 8:31 GMT-03:00 Ricardo Bastos C. Prudencio : > New deadline for paper submission: April 30th > > ========================================================= > > Call for Papers - BRACIS/ENIAC 2014 > > ========================================================= > BRACIS 2014 - Brazilian Conference on Intelligent Systems > > ENIAC 2014 - Encontro Nacional de Intelig?ncia Artificial e Computacional > > October 18-23, 2014 - S?o Carlos-SP, Brazil > > http://jcris2014.icmc.usp.br/index.php/bracis-eniac > > ========================================================= > > Submission > > BRACIS\ENIAC will have a unified submission process. Papers should be > written in English or Portuguese. Submitted papers must not exceed 6 pages, > including all tables, figures, and references (style of the IEEE Computer > Society Press). At maximum, two additional pages are permitted with > overlength page charge. Formatting instructions, as well as templates for > Word and /LaTeX/, can be found in > > http://www.computer.org/portal/web/cscps/formatting > > The best papers in English will be included in the BRACIS proceedings > published by IEEE. All accepted papers written in Portuguese will appear in > the ENIAC proceedings, published electronically as BDBComp. The remaining > accepted papers submitted in English, but not indicated to BRACIS will be > suggested for publication in ENIAC. > > Authors of selected papers will be invited to submit extended versions of > their work to be appreciated for publication in special issues of several > international journals (to be announced). > > Papers must be submitted in PDF files through the EasyChair system. > > https://www.easychair.org/conferences/?conf=braciseniac2014 > > The deadline for paper registration/upload is April 30th., 23:55 BRST. > > > Submission Policy > > By submitting papers to BRACIS/ENIAC 2014, the authors agree that in case > of acceptance, at least one author should be fully registered to the > conference **before** the deadline for sending the camera-ready paper. > Accepted papers without the respective author full registration **before** > the deadline will not be included in the proceedings. > > > Important dates > > Deadline Submission for Regular Papers: April 30, 2014 (New deadline) > Acceptance notification: June 12, 2014 > Final camera-ready papers due: July 7, 2014 > > > Program Chairs: > > Ricardo Prudencio (UFPE) and Paulo E. Santos (FEI) > > Local BRACIS Chair: Estevam Rafael Hruschka Junior (UFSCar) and Heloisa > de Arruda Camargo (UFSCar) > > > > -- Ricardo Prud?ncio. Prof. Associado I - Centro de Inform?tica Universidade Federal de Pernambuco Recife (PE) - Brasil http://www.cin.ufpe.br/~rbcp/ https://twitter.com/#!/ricardobcp -------------- next part -------------- An HTML attachment was scrubbed... URL: From sam.devlin at york.ac.uk Wed Mar 19 07:36:49 2014 From: sam.devlin at york.ac.uk (Sam Devlin) Date: Wed, 19 Mar 2014 11:36:49 +0000 Subject: Connectionists: Call for Participation: Adaptive Learning Agents Workshop @ AAMAS 2014 Message-ID: *Call For Participation* *Adaptive and Learning Agents Workshop 2014 * *at AAMAS 2014 (Paris, France)* *May 5-6* ******************************************************* Register at: http://aamas2014.lip6.fr/registration.php ******************************************************* ALA 2014: Adaptive and Learning Agents Workshop held at AAMAS 2014 (Paris, France). The ALA workshop has a long and successful history and is now in its 14th edition. The workshop is a merger of European ALAMAS and the American ALAg series which is usually held at AAMAS. Our technical program will include 18 oral presentations, an invited speaker and 2 tutorial sessions on multi-agent reinforcement learning and reward shaping (difference rewards and potential-based reward shaping). More details may be found on the workshop web site: http://swarmlab.unimaas.nl/ala2014/ ******************************************************* * Early Registration Deadline: March 25, 2014 * Workshop: May 5-6, 2014 Register at: http://aamas2014.lip6.fr/registration.php ******************************************************* Adaptive and Learning Agents, particularly those in a multi-agent setting are becoming more and more prominent as the sheer size and complexity of many real world systems grows. How to adaptively control, coordinate and optimize such systems is an emerging multi-disciplinary research area at the intersection of Computer Science, Control theory, Economics, and Biology. The ALA workshop will focus on agent and multi-agent systems which employ learning or adaptation. The goal of this workshop is to increase awareness and interest in adaptive agent research, encourage collaboration and give a representative overview of current research in the area of adaptive and learning agents and multi-agent systems. It aims at bringing together not only scientists from different areas of computer science but also from different fields studying similar concepts (e.g., game theory, bio-inspired control, mechanism design). This workshop will focus on all aspects of adaptive and learning agents and multi-agent systems with a particular emphasis on how to modify established learning techniques and/or create new learning paradigms to address the many challenges presented by complex real-world problems. The topics of interest include but are not limited to: * Novel combinations of reinforcement and supervised learning approaches * Integrated learning approaches that work with other agent reasoning modules like negotiation, trust models, coordination, etc. * Supervised multi-agent learning * Reinforcement learning (single and multi-agent) * Planning (single and multi-agent) * Reasoning (single and multi-agent) * Distributed learning * Adaptation and learning in dynamic environments * Evolution of agents in complex environments * Co-evolution of agents in a multi-agent setting * Cooperative exploration and learning to cooperate and collaborate * Learning trust and reputation * Communication restrictions and their impact on multi-agent coordination * Design of reward structure and fitness measures for coordination * Scaling learning techniques to large systems of learning and adaptive agents * Emergent behaviour in adaptive multi-agent systems * Game theoretical analysis of adaptive multi-agent systems * Neuro-control in multi-agent systems * Bio-inspired multi-agent systems * Applications of adaptive and learning agents and multi-agent systems to real world complex systems * Learning of Co-ordination ******************************************************* Organization *Workshop chairs:* Samuel Barrett (The University of Texas at Austin, USA) Sam Devlin (University of York, UK) Daniel Hennes (European Space Agency, The Netherlands) If you have any questions about the ALA workshop, please contact the organizers at: ala.workshop.2014 AT gmail.com *Senior Steering Committee Members:* Daniel Kudenko (University of York, UK) Ann Now? (Vrije Universiteit Brussels, Belgium) Peter Stone (University of Texas at Austin, USA) Matthew Taylor (Lafayette College, USA) Kagan Tumer (Oregon State University, USA) Karl Tuyls (Maastricht University, The Netherlands) ******************************************************* -- Dr Sam Devlin Research Associate York Centre for Complex Systems Analysis The University of York Deramore Lane, York, YO10 5GH w: http://www.cs.york.ac.uk/~devlin/ Disclaimer: http://www.york.ac.uk/docs/disclaimer/email.htm -------------- next part -------------- An HTML attachment was scrubbed... URL: From weng at cse.msu.edu Wed Mar 19 13:50:34 2014 From: weng at cse.msu.edu (Juyang Weng) Date: Wed, 19 Mar 2014 13:50:34 -0400 Subject: Connectionists: how the brain works? In-Reply-To: <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> Message-ID: <5329D8EA.8040901@cse.msu.edu> Mike, Yes, they are very different in the signals they receive and process after at least several months' development prenatally, but this is not a sufficiently deep causality for us to truly understand how the brain works. Cerebral cortex, hippocampus and cerebellum are all very similar in the mechanisms that enable them to develop into what they are, prenatally and postnatally. An intuitive way to think of this deeper causality is: Development is cell-based. The same set of cell properties enables cells to migrate, connect and form cerebral cortex, hippocampus and cerebellum while each cell taking signals from other cells. -John On 3/14/14 3:40 PM, Michael Arbib wrote: > At 11:17 AM 3/14/2014, Juyang Weng wrote: >> The brain uses a single architecture to do all brain functions we are >> aware of! It uses the same architecture to do vision, audition, >> motor, reasoning, decision making, motivation (including pain >> avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). > > Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were > very different from each other. > -- -- Juyang (John) Weng, Professor Department of Computer Science and Engineering MSU Cognitive Science Program and MSU Neuroscience Program 428 S Shaw Ln Rm 3115 Michigan State University East Lansing, MI 48824 USA Tel: 517-353-4388 Fax: 517-432-1061 Email: weng at cse.msu.edu URL: http://www.cse.msu.edu/~weng/ ---------------------------------------------- From bower at uthscsa.edu Wed Mar 19 14:42:48 2014 From: bower at uthscsa.edu (james bower) Date: Wed, 19 Mar 2014 13:42:48 -0500 Subject: Connectionists: how the brain works? In-Reply-To: <5329D8EA.8040901@cse.msu.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> Message-ID: <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> Actually, the previous statement is only true in its most abstract form -which in that form also applies to the heart, the kidney and trees too. So not sure what use that is. (trees used cellular based communication to react to predation by insects - and at least mine look like they are in pain when they do so). the further statement about similar developmental processes for cortical like brain structures is also only true in its most abstract sense. In particular, the cerebellum has a quite unique form of cortical development (very different from the frontal cortical structures. cell migration patterns, the way cellular components get connected, as well as general timing - all of which are almost certainly important to its function. The cerebellum, for example, largely develops entirely postnatally in most mammals. It is also important to note that cerebellar development is also considerably better understood than is the case for cerebral cortex. Again, as I have argued many times before - in biology (perhaps unfortunately) the devil (and therefore the computation) is in the details. Gloss over them at your risk. Jim On Mar 19, 2014, at 12:50 PM, Juyang Weng wrote: > Mike, > > Yes, they are very different in the signals they receive and process after at least several months' development prenatally, but this is > not a sufficiently deep causality for us to truly understand how the brain works. Cerebral cortex, hippocampus and cerebellum are all very similar in the mechanisms that enable them to develop into what they are, prenatally and postnatally. > > An intuitive way to think of this deeper causality is: Development is cell-based. The same set of cell properties enables cells to migrate, connect and form cerebral cortex, hippocampus and cerebellum while each cell taking signals from other cells. > > -John > > On 3/14/14 3:40 PM, Michael Arbib wrote: >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >>> The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very different from each other. >> > > -- > -- > Juyang (John) Weng, Professor > Department of Computer Science and Engineering > MSU Cognitive Science Program and MSU Neuroscience Program > 428 S Shaw Ln Rm 3115 > Michigan State University > East Lansing, MI 48824 USA > Tel: 517-353-4388 > Fax: 517-432-1061 > Email: weng at cse.msu.edu > URL: http://www.cse.msu.edu/~weng/ > ---------------------------------------------- > From brian.mingus at colorado.edu Wed Mar 19 16:07:48 2014 From: brian.mingus at colorado.edu (Brian J Mingus) Date: Wed, 19 Mar 2014 14:07:48 -0600 Subject: Connectionists: how the brain works? In-Reply-To: <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> Message-ID: Hi Jim, Focusing too much on the details is risky in and of itself. Optimal compression requires a balance, and we can't compute what that balance is (all models are wrong). One thing we can say for sure is that we should err on the side of simplicity, and adding detail to theories before simpler explanations have failed is not Ockham's heuristic. That said it's still in the space of a Big Data fuzzy science approach, where we throw as much data from as many levels of analysis as we can come up with into a big pot and then construct a theory. The thing to keep in mind is that when we start pruning this model most of the details are going to disappear, because almost all of them are irrelevant. Indeed, the size of the description that includes all the details is almost infinite, whereas the length of the description that explains almost all the variance is extremely short, especially in comparison. This is why Ockham's razor is a good heuristic. It helps prevent us from wasting time on unnecessary details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. Brian On Wed, Mar 19, 2014 at 12:42 PM, james bower wrote: > Actually, the previous statement is only true in its most abstract form > -which in that form also applies to the heart, the kidney and trees too. > So not sure what use that is. (trees used cellular based communication to > react to predation by insects - and at least mine look like they are in > pain when they do so). > > > the further statement about similar developmental processes for cortical > like brain structures is also only true in its most abstract sense. In > particular, the cerebellum has a quite unique form of cortical development > (very different from the frontal cortical structures. cell migration > patterns, the way cellular components get connected, as well as general > timing - all of which are almost certainly important to its function. The > cerebellum, for example, largely develops entirely postnatally in most > mammals. It is also important to note that cerebellar development is also > considerably better understood than is the case for cerebral cortex. > > Again, as I have argued many times before - in biology (perhaps > unfortunately) the devil (and therefore the computation) is in the details. > Gloss over them at your risk. > > Jim > > > > > > On Mar 19, 2014, at 12:50 PM, Juyang Weng wrote: > > > Mike, > > > > Yes, they are very different in the signals they receive and process > after at least several months' development prenatally, but this is > > not a sufficiently deep causality for us to truly understand how the > brain works. Cerebral cortex, hippocampus and cerebellum are all very > similar in the mechanisms that enable them to develop into what they are, > prenatally and postnatally. > > > > An intuitive way to think of this deeper causality is: Development is > cell-based. The same set of cell properties enables cells to migrate, > connect and form cerebral cortex, hippocampus and cerebellum while each > cell taking signals from other cells. > > > > -John > > > > On 3/14/14 3:40 PM, Michael Arbib wrote: > >> At 11:17 AM 3/14/2014, Juyang Weng wrote: > >>> The brain uses a single architecture to do all brain functions we are > aware of! It uses the same architecture to do vision, audition, motor, > reasoning, decision making, motivation (including pain avoidance and > pleasure seeking, novelty seeking, higher emotion, etc.). > >> > >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were > very different from each other. > >> > > > > -- > > -- > > Juyang (John) Weng, Professor > > Department of Computer Science and Engineering > > MSU Cognitive Science Program and MSU Neuroscience Program > > 428 S Shaw Ln Rm 3115 > > Michigan State University > > East Lansing, MI 48824 USA > > Tel: 517-353-4388 > > Fax: 517-432-1061 > > Email: weng at cse.msu.edu > > URL: http://www.cse.msu.edu/~weng/ > > ---------------------------------------------- > > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From danko.nikolic at googlemail.com Wed Mar 19 16:38:49 2014 From: danko.nikolic at googlemail.com (Danko Nikolic) Date: Wed, 19 Mar 2014 21:38:49 +0100 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> Message-ID: <532A0059.9020604@gmail.com> Hi all, The problem of detailed vs. abstract forms that is being discussed is in the heart of practopoietic theory: It addresses that problem in a way similar to the distinction between genotype and phenotype. For example, if the basic architectural principles of cortex would correspond to genotype, then the specific variation due to a particular sensory modality would correspond to phenotype. Practopoiesis generalizes these genotype-phenotype--like relations to all levels of system organization: It defines hierarchical organization of cybernetic knowledge, each higher level possessing more specific version of the knowledge provided by the preceding one. Practopoiesis suggests that the most interesting part is not a choice of describing the system either with detailed or with abstract operations. Instead, the process of transition from abstract to details is the important one to understand. This transition process, called 'traverse', is responsible for development of the organism, learning new knowledge, execution of cognitive operations, and generation of behavior. In each case, some general knowledge gets instantiated into more specific one. Practopoiesis explains how this happens within a hierarchy, and what the role of a continuous interaction with the environment is. Danko On 3/19/2014 9:07 PM, Brian J Mingus wrote: > Hi Jim, > > Focusing too much on the details is risky in and of itself. Optimal > compression requires a balance, and we can't compute what that balance > is (all models are wrong). One thing we can say for sure is that we > should err on the side of simplicity, and adding detail to theories > before simpler explanations have failed is not Ockham's heuristic. > That said it's still in the space of a Big Data fuzzy science > approach, where we throw as much data from as many levels of analysis > as we can come up with into a big pot and then construct a theory. The > thing to keep in mind is that when we start pruning this model most of > the details are going to disappear, because almost all of them are > irrelevant. Indeed, the size of the description that includes all the > details is almost infinite, whereas the length of the description that > explains almost all the variance is extremely short, especially in > comparison. This is why Ockham's razor is a good heuristic. It helps > prevent us from wasting time on unnecessary details by suggesting that > we only inquire as to the details once our existing simpler theory has > failed to work. > > Brian > > > On Wed, Mar 19, 2014 at 12:42 PM, james bower > wrote: > > Actually, the previous statement is only true in its most abstract > form -which in that form also applies to the heart, the kidney and > trees too. So not sure what use that is. (trees used cellular > based communication to react to predation by insects - and at > least mine look like they are in pain when they do so). > > > the further statement about similar developmental processes for > cortical like brain structures is also only true in its most > abstract sense. In particular, the cerebellum has a quite unique > form of cortical development (very different from the frontal > cortical structures. cell migration patterns, the way cellular > components get connected, as well as general timing - all of which > are almost certainly important to its function. The cerebellum, > for example, largely develops entirely postnatally in most > mammals. It is also important to note that cerebellar development > is also considerably better understood than is the case for > cerebral cortex. > > Again, as I have argued many times before - in biology (perhaps > unfortunately) the devil (and therefore the computation) is in the > details. Gloss over them at your risk. > > Jim > > > > > > On Mar 19, 2014, at 12:50 PM, Juyang Weng > wrote: > > > Mike, > > > > Yes, they are very different in the signals they receive and > process after at least several months' development prenatally, but > this is > > not a sufficiently deep causality for us to truly understand how > the brain works. Cerebral cortex, hippocampus and cerebellum are > all very similar in the mechanisms that enable them to develop > into what they are, prenatally and postnatally. > > > > An intuitive way to think of this deeper causality is: > Development is cell-based. The same set of cell properties > enables cells to migrate, connect and form cerebral cortex, > hippocampus and cerebellum while each cell taking signals from > other cells. > > > > -John > > > > On 3/14/14 3:40 PM, Michael Arbib wrote: > >> At 11:17 AM 3/14/2014, Juyang Weng wrote: > >>> The brain uses a single architecture to do all brain functions > we are aware of! It uses the same architecture to do vision, > audition, motor, reasoning, decision making, motivation (including > pain avoidance and pleasure seeking, novelty seeking, higher > emotion, etc.). > >> > >> Gosh -- and I thought cerebral cortex, hippocampus and > cerebellum were very different from each other. > >> > > > > -- > > -- > > Juyang (John) Weng, Professor > > Department of Computer Science and Engineering > > MSU Cognitive Science Program and MSU Neuroscience Program > > 428 S Shaw Ln Rm 3115 > > Michigan State University > > East Lansing, MI 48824 USA > > Tel: 517-353-4388 > > Fax: 517-432-1061 > > Email: weng at cse.msu.edu > > URL: http://www.cse.msu.edu/~weng/ > > ---------------------------------------------- > > > > > -- Prof. Dr. Danko Nikolic Web: http://www.danko-nikolic.com Mail address 1: Department of Neurophysiology Max Planck Institut for Brain Research Deutschordenstr. 46 60528 Frankfurt am Main GERMANY Mail address 2: Frankfurt Institute for Advanced Studies Wolfgang Goethe University Ruth-Moufang-Str. 1 60433 Frankfurt am Main GERMANY ---------------------------- Office: (..49-69) 96769-736 Lab: (..49-69) 96769-209 Fax: (..49-69) 96769-327 danko.nikolic at gmail.com ---------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From brian.mingus at colorado.edu Wed Mar 19 16:55:01 2014 From: brian.mingus at colorado.edu (Brian J Mingus) Date: Wed, 19 Mar 2014 14:55:01 -0600 Subject: Connectionists: how the brain works? In-Reply-To: <532A0059.9020604@gmail.com> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <532A0059.9020604@gmail.com> Message-ID: Hi Danko, I think I grok what you are saying and this sounds like a useful contribution to me. That said, I don't think most folks are interested in understanding the entire organism, and indeed, such an endeavor would seem to require an almost complete description of reality. Personally, I just want to create a happy being that can think faster than me and answer my philosophical questions and lend a hand with solving physics problems. How will practopoiesis help me do this, beyond me using basic heuristics from psychology, such as just taking a quick look at lesion studies and psychopathologies, which can help inform which parts of the brain, and which details I need to include? For example, I already have a quite functional system that seems to accomplish the same thing as practopoiesis. I call the "genotype" the first principle component, and the "phenotype" all the rest of the components. Cheers, Brian On Wed, Mar 19, 2014 at 2:38 PM, Danko Nikolic wrote: > Hi all, > > The problem of detailed vs. abstract forms that is being discussed is in > the heart of practopoietic theory: It addresses that problem in a way > similar to the distinction between genotype and phenotype. For example, if > the basic architectural principles of cortex would correspond to genotype, > then the specific variation due to a particular sensory modality would > correspond to phenotype. Practopoiesis generalizes these > genotype-phenotype--like relations to all levels of system organization: It > defines hierarchical organization of cybernetic knowledge, each higher > level possessing more specific version of the knowledge provided by the > preceding one. > > Practopoiesis suggests that the most interesting part is not a choice of > describing the system either with detailed or with abstract operations. > Instead, the process of transition from abstract to details is the > important one to understand. This transition process, called 'traverse', is > responsible for development of the organism, learning new knowledge, > execution of cognitive operations, and generation of behavior. In each > case, some general knowledge gets instantiated into more specific one. > Practopoiesis explains how this happens within a hierarchy, and what the > role of a continuous interaction with the environment is. > > Danko > > > > On 3/19/2014 9:07 PM, Brian J Mingus wrote: > > Hi Jim, > > Focusing too much on the details is risky in and of itself. Optimal > compression requires a balance, and we can't compute what that balance is > (all models are wrong). One thing we can say for sure is that we should err > on the side of simplicity, and adding detail to theories before simpler > explanations have failed is not Ockham's heuristic. That said it's still in > the space of a Big Data fuzzy science approach, where we throw as much data > from as many levels of analysis as we can come up with into a big pot and > then construct a theory. The thing to keep in mind is that when we start > pruning this model most of the details are going to disappear, because > almost all of them are irrelevant. Indeed, the size of the description that > includes all the details is almost infinite, whereas the length of the > description that explains almost all the variance is extremely short, > especially in comparison. This is why Ockham's razor is a good heuristic. > It helps prevent us from wasting time on unnecessary details by suggesting > that we only inquire as to the details once our existing simpler theory has > failed to work. > > Brian > > > On Wed, Mar 19, 2014 at 12:42 PM, james bower wrote: > >> Actually, the previous statement is only true in its most abstract form >> -which in that form also applies to the heart, the kidney and trees too. >> So not sure what use that is. (trees used cellular based communication to >> react to predation by insects - and at least mine look like they are in >> pain when they do so). >> >> >> the further statement about similar developmental processes for cortical >> like brain structures is also only true in its most abstract sense. In >> particular, the cerebellum has a quite unique form of cortical development >> (very different from the frontal cortical structures. cell migration >> patterns, the way cellular components get connected, as well as general >> timing - all of which are almost certainly important to its function. The >> cerebellum, for example, largely develops entirely postnatally in most >> mammals. It is also important to note that cerebellar development is also >> considerably better understood than is the case for cerebral cortex. >> >> Again, as I have argued many times before - in biology (perhaps >> unfortunately) the devil (and therefore the computation) is in the details. >> Gloss over them at your risk. >> >> Jim >> >> >> >> >> >> On Mar 19, 2014, at 12:50 PM, Juyang Weng wrote: >> >> > Mike, >> > >> > Yes, they are very different in the signals they receive and process >> after at least several months' development prenatally, but this is >> > not a sufficiently deep causality for us to truly understand how the >> brain works. Cerebral cortex, hippocampus and cerebellum are all very >> similar in the mechanisms that enable them to develop into what they are, >> prenatally and postnatally. >> > >> > An intuitive way to think of this deeper causality is: Development is >> cell-based. The same set of cell properties enables cells to migrate, >> connect and form cerebral cortex, hippocampus and cerebellum while each >> cell taking signals from other cells. >> > >> > -John >> > >> > On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >>> The brain uses a single architecture to do all brain functions we are >> aware of! It uses the same architecture to do vision, audition, motor, >> reasoning, decision making, motivation (including pain avoidance and >> pleasure seeking, novelty seeking, higher emotion, etc.). >> >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were >> very different from each other. >> >> >> > >> > -- >> > -- >> > Juyang (John) Weng, Professor >> > Department of Computer Science and Engineering >> > MSU Cognitive Science Program and MSU Neuroscience Program >> > 428 S Shaw Ln Rm 3115 >> > Michigan State University >> > East Lansing, MI 48824 USA >> > Tel: 517-353-4388 >> > Fax: 517-432-1061 >> > Email: weng at cse.msu.edu >> > URL: http://www.cse.msu.edu/~weng/ >> > ---------------------------------------------- >> > >> >> >> > > -- > > Prof. Dr. Danko Nikolic > > > Web: http://www.danko-nikolic.com > > > Mail address 1: > Department of Neurophysiology > Max Planck Institut for Brain Research > Deutschordenstr. 46 > 60528 Frankfurt am Main > GERMANY > > Mail address 2: > Frankfurt Institute for Advanced Studies > Wolfgang Goethe University > Ruth-Moufang-Str. 1 > 60433 Frankfurt am Main > GERMANY > > ---------------------------- > Office: (..49-69) 96769-736 > Lab: (..49-69) 96769-209 > Fax: (..49-69) 96769-327danko.nikolic at gmail.com > ---------------------------- > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From danko.nikolic at googlemail.com Wed Mar 19 18:03:17 2014 From: danko.nikolic at googlemail.com (Danko Nikolic) Date: Wed, 19 Mar 2014 23:03:17 +0100 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <532A0059.9020604@gmail.com> Message-ID: <532A1425.9090005@gmail.com> Hi Brian, Practopoiesis is not a theory of the entire organism such that it would be some sort of an overkill for explaining the brain and behavior. Practopoiesis is primarily a theory of how the brain creates behavior. It was just to my surprise to discover post hoc that these same principles apply to the rest of biology (including genotype-phenotype relation). You asked: "Personally, I just want to create a happy being that can think faster than me and answer my philosophical questions and lend a hand with solving physics problems. How will practopoiesis help me do this, beyond me using basic heuristics from psychology, such as just taking a quick look at lesion studies and psychopathologies, which can help inform which parts of the brain, and which details I need to include?" Practopoiesis tells you why the current approaches did not work so far. It explains what was missing. It also describes some of the properties of this missing component so that one could go ahead and look for it in the brain. Also, it describes the contribution of this component to working memory, attention, semantics and to a few other aspects of cognition (I am not sure about happiness though). You wrote: "For example, I already have a quite functional system that seems to accomplish the same thing as practopoiesis. I call the "genotype" the first principle component, and the "phenotype" all the rest of the components." The first principle component of brain network variance is an interesting idea of compressing a brain model. You could use it also in a practopoietic system. However, practopoiesis tells you that you will need something else in addition. The network of the brain, irrespective of whether it is compressed or not, can produce only one traverse. Plasticity can add another traverse. So, you would then have in total two traverses. Practopoiesis tells you that you need in total three traverses. You can think of it as three stages of transition from genotype to phenotype, whereby in each new transition, the previous phenotype plays a role of genotype of the new phenotype, and so on. Thus you would need to scan three different levels of the brain: network + plasticity + one more named 'anapoiesis' (and then perhaps one can use your idea and compress each by PCA). Practopoietic theory explains why you need three and why one or two are not enough. Danko On 3/19/2014 9:55 PM, Brian J Mingus wrote: > Hi Danko, > > I think I grok what you are saying and this sounds like a useful > contribution to me. That said, I don't think most folks are interested > in understanding the entire organism, and indeed, such an endeavor > would seem to require an almost complete description of reality. > Personally, I just want to create a happy being that can think faster > than me and answer my philosophical questions and lend a hand with > solving physics problems. How will practopoiesis help me do this, > beyond me using basic heuristics from psychology, such as just taking > a quick look at lesion studies and psychopathologies, which can help > inform which parts of the brain, and which details I need to include? > > For example, I already have a quite functional system that seems to > accomplish the same thing as practopoiesis. I call the "genotype" the > first principle component, and the "phenotype" all the rest of the > components. > > Cheers, > > Brian > > > On Wed, Mar 19, 2014 at 2:38 PM, Danko Nikolic > > > wrote: > > Hi all, > > The problem of detailed vs. abstract forms that is being discussed > is in the heart of practopoietic theory: It addresses that problem > in a way similar to the distinction between genotype and > phenotype. For example, if the basic architectural principles of > cortex would correspond to genotype, then the specific variation > due to a particular sensory modality would correspond to > phenotype. Practopoiesis generalizes these > genotype-phenotype--like relations to all levels of system > organization: It defines hierarchical organization of cybernetic > knowledge, each higher level possessing more specific version of > the knowledge provided by the preceding one. > > Practopoiesis suggests that the most interesting part is not a > choice of describing the system either with detailed or with > abstract operations. Instead, the process of transition from > abstract to details is the important one to understand. This > transition process, called 'traverse', is responsible for > development of the organism, learning new knowledge, execution of > cognitive operations, and generation of behavior. In each case, > some general knowledge gets instantiated into more specific one. > Practopoiesis explains how this happens within a hierarchy, and > what the role of a continuous interaction with the environment is. > > Danko > > > > On 3/19/2014 9:07 PM, Brian J Mingus wrote: >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. >> Optimal compression requires a balance, and we can't compute what >> that balance is (all models are wrong). One thing we can say for >> sure is that we should err on the side of simplicity, and adding >> detail to theories before simpler explanations have failed is not >> Ockham's heuristic. That said it's still in the space of a Big >> Data fuzzy science approach, where we throw as much data from as >> many levels of analysis as we can come up with into a big pot and >> then construct a theory. The thing to keep in mind is that when >> we start pruning this model most of the details are going to >> disappear, because almost all of them are irrelevant. Indeed, the >> size of the description that includes all the details is almost >> infinite, whereas the length of the description that explains >> almost all the variance is extremely short, especially in >> comparison. This is why Ockham's razor is a good heuristic. It >> helps prevent us from wasting time on unnecessary details by >> suggesting that we only inquire as to the details once our >> existing simpler theory has failed to work. >> >> Brian >> >> >> On Wed, Mar 19, 2014 at 12:42 PM, james bower > > wrote: >> >> Actually, the previous statement is only true in its most >> abstract form -which in that form also applies to the heart, >> the kidney and trees too. So not sure what use that is. >> (trees used cellular based communication to react to >> predation by insects - and at least mine look like they are >> in pain when they do so). >> >> >> the further statement about similar developmental processes >> for cortical like brain structures is also only true in its >> most abstract sense. In particular, the cerebellum has a >> quite unique form of cortical development (very different >> from the frontal cortical structures. cell migration >> patterns, the way cellular components get connected, as well >> as general timing - all of which are almost certainly >> important to its function. The cerebellum, for example, >> largely develops entirely postnatally in most mammals. It is >> also important to note that cerebellar development is also >> considerably better understood than is the case for cerebral >> cortex. >> >> Again, as I have argued many times before - in biology >> (perhaps unfortunately) the devil (and therefore the >> computation) is in the details. Gloss over them at your risk. >> >> Jim >> >> >> >> >> >> On Mar 19, 2014, at 12:50 PM, Juyang Weng > > wrote: >> >> > Mike, >> > >> > Yes, they are very different in the signals they receive >> and process after at least several months' development >> prenatally, but this is >> > not a sufficiently deep causality for us to truly >> understand how the brain works. Cerebral cortex, hippocampus >> and cerebellum are all very similar in the mechanisms that >> enable them to develop into what they are, prenatally and >> postnatally. >> > >> > An intuitive way to think of this deeper causality is: >> Development is cell-based. The same set of cell properties >> enables cells to migrate, connect and form cerebral cortex, >> hippocampus and cerebellum while each cell taking signals >> from other cells. >> > >> > -John >> > >> > On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >>> The brain uses a single architecture to do all brain >> functions we are aware of! It uses the same architecture to >> do vision, audition, motor, reasoning, decision making, >> motivation (including pain avoidance and pleasure seeking, >> novelty seeking, higher emotion, etc.). >> >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and >> cerebellum were very different from each other. >> >> >> > >> > -- >> > -- >> > Juyang (John) Weng, Professor >> > Department of Computer Science and Engineering >> > MSU Cognitive Science Program and MSU Neuroscience Program >> > 428 S Shaw Ln Rm 3115 >> > Michigan State University >> > East Lansing, MI 48824 USA >> > Tel: 517-353-4388 >> > Fax: 517-432-1061 >> > Email: weng at cse.msu.edu >> > URL: http://www.cse.msu.edu/~weng/ >> >> > ---------------------------------------------- >> > >> >> >> > > -- > > Prof. Dr. Danko Nikolic > > > Web:http://www.danko-nikolic.com > Mail address 1: Department of Neurophysiology Max Planck Institut > for Brain Research Deutschordenstr. 46 60528 Frankfurt am Main > GERMANY Mail address 2: Frankfurt Institute for Advanced Studies > Wolfgang Goethe University Ruth-Moufang-Str. 1 60433 Frankfurt am > Main GERMANY ---------------------------- Office: (..49-69) > 96769-736 Lab: (..49-69) 96769-209 Fax: (..49-69) 96769-327 > danko.nikolic at gmail.com > ---------------------------- > > -- Prof. Dr. Danko Nikolic Web: http://www.danko-nikolic.com Mail address 1: Department of Neurophysiology Max Planck Institut for Brain Research Deutschordenstr. 46 60528 Frankfurt am Main GERMANY Mail address 2: Frankfurt Institute for Advanced Studies Wolfgang Goethe University Ruth-Moufang-Str. 1 60433 Frankfurt am Main GERMANY ---------------------------- Office: (..49-69) 96769-736 Lab: (..49-69) 96769-209 Fax: (..49-69) 96769-327 danko.nikolic at gmail.com ---------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From bower at uthscsa.edu Wed Mar 19 18:05:24 2014 From: bower at uthscsa.edu (james bower) Date: Wed, 19 Mar 2014 17:05:24 -0500 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> Message-ID: <13364C7D-6061-4FEB-B918-3DD2328C954D@uthscsa.edu> As I have discussed (some would say ad nauseam) on this list serve before, Ockham?s razor was a terrible heuristic for planetary science for about 1500 years, not with respect to replicating the position of the planets in the night sky (big data ?prediction? problem as it would currently be called now) but instead as a model to actually understand planetary motion, which became the foundation for mechanics and modern physics. thank heavens that Kepler (who was predisposed) and Newton (who was not) didn?t apply the same argument below (actually Copernicus sort of did ironically enough). I believe that there is a very important lesson there for those of us who are actually trying to figure out how the brain works. Simplifying for the sake of it is even less appropriate in a physical system (the brain or any biological system) that explicitly uses order and complexity to do what it does (mess with the second law). The details matter, and the details are all we really have. You can invent any kind of model you want to ?predict? (as I have said previously actually usually ?postdict?) the data - however, history suggests and in my own experience it won?t get you very far in figuring out how things actually work. In fact, models of this sort distort the field and have resulting in years of running down blind alleys. Old expression (I didn?t make up) "in biology Ockam?s Razor will cut your throat". For those of you interested, I just posted an essay on this general subject recently written for a publication whose editors decided not to publish: http://jamesmbower.com/blog/?p=119 feel free to take a look - but I warn most of you - it will probably make your teeth grind. but this: "we throw as much data from as many levels of analysis as we can come up with into a big pot and then construct a theory? makes my blood boil for the outright stupidity. Why create an unstructed mess, when you actually have structure to look at - as complex as it is THAT SHOULD TELL YOU SOMETHING> And from long experience in building models of the nervous system, the precise reason you include the details is because you don?t know what doesn?t matter. And, in fact, remarkably enough, so far, they all have in completely unexpected ways. That, of course, is the point. Biology works because of the details - selection is precisely a mechanism that selects for the details, just as the rather remarkable system of biological coding preserves them and replicates them. If someone wants to declare that all parts of the brain basically work the same way, they are so far off the mark it is hardly worth responding - although, of course, I keep doing so (tilting at windmills) Perhaps the unindoctrinated interested in these subjects will at least spend the minimal amount of time necessary to understand something about the remarkably fine and detailed structure of the brain - WHICH IS THERE FOR A REASON. Arggg jim On Mar 19, 2014, at 3:07 PM, Brian J Mingus wrote: > Hi Jim, > > Focusing too much on the details is risky in and of itself. Optimal compression requires a balance, and we can't compute what that balance is (all models are wrong). One thing we can say for sure is that we should err on the side of simplicity, and adding detail to theories before simpler explanations have failed is not Ockham's heuristic. That said it's still in the space of a Big Data fuzzy science approach, where we throw as much data from as many levels of analysis as we can come up with into a big pot and then construct a theory. The thing to keep in mind is that when we start pruning this model most of the details are going to disappear, because almost all of them are irrelevant. Indeed, the size of the description that includes all the details is almost infinite, whereas the length of the description that explains almost all the variance is extremely short, especially in comparison. This is why Ockham's razor is a good heuristic. It helps prevent us from wasting time on unnecessary details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. > > Brian > > > On Wed, Mar 19, 2014 at 12:42 PM, james bower wrote: > Actually, the previous statement is only true in its most abstract form -which in that form also applies to the heart, the kidney and trees too. So not sure what use that is. (trees used cellular based communication to react to predation by insects - and at least mine look like they are in pain when they do so). > > > the further statement about similar developmental processes for cortical like brain structures is also only true in its most abstract sense. In particular, the cerebellum has a quite unique form of cortical development (very different from the frontal cortical structures. cell migration patterns, the way cellular components get connected, as well as general timing - all of which are almost certainly important to its function. The cerebellum, for example, largely develops entirely postnatally in most mammals. It is also important to note that cerebellar development is also considerably better understood than is the case for cerebral cortex. > > Again, as I have argued many times before - in biology (perhaps unfortunately) the devil (and therefore the computation) is in the details. Gloss over them at your risk. > > Jim > > > > > > On Mar 19, 2014, at 12:50 PM, Juyang Weng wrote: > > > Mike, > > > > Yes, they are very different in the signals they receive and process after at least several months' development prenatally, but this is > > not a sufficiently deep causality for us to truly understand how the brain works. Cerebral cortex, hippocampus and cerebellum are all very similar in the mechanisms that enable them to develop into what they are, prenatally and postnatally. > > > > An intuitive way to think of this deeper causality is: Development is cell-based. The same set of cell properties enables cells to migrate, connect and form cerebral cortex, hippocampus and cerebellum while each cell taking signals from other cells. > > > > -John > > > > On 3/14/14 3:40 PM, Michael Arbib wrote: > >> At 11:17 AM 3/14/2014, Juyang Weng wrote: > >>> The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). > >> > >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very different from each other. > >> > > > > -- > > -- > > Juyang (John) Weng, Professor > > Department of Computer Science and Engineering > > MSU Cognitive Science Program and MSU Neuroscience Program > > 428 S Shaw Ln Rm 3115 > > Michigan State University > > East Lansing, MI 48824 USA > > Tel: 517-353-4388 > > Fax: 517-432-1061 > > Email: weng at cse.msu.edu > > URL: http://www.cse.msu.edu/~weng/ > > ---------------------------------------------- > > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From brian.mingus at colorado.edu Wed Mar 19 19:13:35 2014 From: brian.mingus at colorado.edu (Brian J Mingus) Date: Wed, 19 Mar 2014 17:13:35 -0600 Subject: Connectionists: how the brain works? In-Reply-To: <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> Message-ID: The hippocampus and cerebellum might be necessary variance. Data from strokes and lesion studies suggest that they are not fully necessary, however. Also, they might be local minima in the design space, and we might be able to replace them with something simpler before we figure out exactly how they work, by first identifying what it is that they do and then inventing something better. Brian On Wed, Mar 19, 2014 at 2:27 PM, Michael Arbib wrote: > Ignoring the gross differences in circuitry between hippocampus and > cerebellum, etc., is not erring on the side of simplicity, it is erring, > period. Have you actually looked at a Cajal/Sxentagothai-style drawing of > their circuitry? > > > At 01:07 PM 3/19/2014, Brian J Mingus wrote: > > Hi Jim, > > Focusing too much on the details is risky in and of itself. Optimal > compression requires a balance, and we can't compute what that balance is > (all models are wrong). One thing we can say for sure is that we should err > on the side of simplicity, and adding detail to theories before simpler > explanations have failed is not Ockham's heuristic. That said it's still in > the space of a Big Data fuzzy science approach, where we throw as much data > from as many levels of analysis as we can come up with into a big pot and > then construct a theory. The thing to keep in mind is that when we start > pruning this model most of the details are going to disappear, because > almost all of them are irrelevant. Indeed, the size of the description that > includes all the details is almost infinite, whereas the length of the > description that explains almost all the variance is extremely short, > especially in comparison. This is why Ockham's razor is a good heuristic. > It helps prevent us from wasting time on unnecessary details by suggesting > that we only inquire as to the details once our existing simpler theory has > failed to work. > > > On 3/14/14 3:40 PM, Michael Arbib wrote: > >> At 11:17 AM 3/14/2014, Juyang Weng wrote: > >>> The brain uses a single architecture to do all brain functions we are > aware of! It uses the same architecture to do vision, audition, motor, > reasoning, decision making, motivation (including pain avoidance and > pleasure seeking, novelty seeking, higher emotion, etc.). > >> > >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were > very different from each other. > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From bower at uthscsa.edu Wed Mar 19 20:00:46 2014 From: bower at uthscsa.edu (james bower) Date: Wed, 19 Mar 2014 19:00:46 -0500 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> Message-ID: ah, someone once said (perhaps it was me) that arrogance and ignorance are a particularly dangerous combination in a scientist - not that there are not am absence of examples. In fact, Michael and I have both been accused of the former, I happen to know, but not generally of the later. :-) However, there is also something to be said for honesty and I am afraid to say that the opinion you express is more commonly held than I would like to think, certainly, it doesn?t sit too far below the surface of many of the so called neuro-morphic models you see floating around, and I can tell you because I was there, that it was pretty apparent in the early days of the Neural Networks business as well. Perhaps worth noting at the same time that some ?notables? in that effort, John Hopfield for example, didn?t share that point of view, neither as far as I can tell did Carver Mead or Richard Feynman for that matter. Collectively the course they taught at Caltech in the early 80s on how the heck to figure out the brain was responsible for Caltech deciding to start the first computational biology graduate program (the CNS program). Those guys were smart enough or knew enough, or both, not to dismiss structures evolved over millions of years, under harsh and unforgiving circumstances that did remarkable things. My advice is that nobody else should either. Jim Bower On Mar 19, 2014, at 6:13 PM, Brian J Mingus wrote: > The hippocampus and cerebellum might be necessary variance. Data from strokes and lesion studies suggest that they are not fully necessary, however. Also, they might be local minima in the design space, and we might be able to replace them with something simpler before we figure out exactly how they work, by first identifying what it is that they do and then inventing something better. > > Brian > > > On Wed, Mar 19, 2014 at 2:27 PM, Michael Arbib wrote: > Ignoring the gross differences in circuitry between hippocampus and cerebellum, etc., is not erring on the side of simplicity, it is erring, period. Have you actually looked at a Cajal/Sxentagothai-style drawing of their circuitry? > > > At 01:07 PM 3/19/2014, Brian J Mingus wrote: >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. Optimal compression requires a balance, and we can't compute what that balance is (all models are wrong). One thing we can say for sure is that we should err on the side of simplicity, and adding detail to theories before simpler explanations have failed is not Ockham's heuristic. That said it's still in the space of a Big Data fuzzy science approach, where we throw as much data from as many levels of analysis as we can come up with into a big pot and then construct a theory. The thing to keep in mind is that when we start pruning this model most of the details are going to disappear, because almost all of them are irrelevant. Indeed, the size of the description that includes all the details is almost infinite, whereas the length of the description that explains almost all the variance is extremely short, especially in comparison. This is why Ockham's razor is a good heuristic. It helps prevent us from wasting time on unnecessary details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. >> >> > On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >>> The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). >> >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very different from each other. > -------------- next part -------------- An HTML attachment was scrubbed... URL: From arbib at usc.edu Wed Mar 19 16:27:09 2014 From: arbib at usc.edu (Michael Arbib) Date: Wed, 19 Mar 2014 13:27:09 -0700 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> Message-ID: <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> An HTML attachment was scrubbed... URL: From bower at uthscsa.edu Wed Mar 19 21:33:29 2014 From: bower at uthscsa.edu (james bower) Date: Wed, 19 Mar 2014 20:33:29 -0500 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> Message-ID: May not apply to the brain for a large set of reasons. On Mar 19, 2014, at 8:29 PM, Brian J Mingus wrote: > The second episode of the new Cosmos provides an excellent example of how evolution gets stuck in local minima that it can't climb out of. > > - Brian > > On Mar 19, 2014 6:00 PM, "james bower" wrote: > ah, someone once said (perhaps it was me) that arrogance and ignorance are a particularly dangerous combination in a scientist - not that there are not am absence of examples. > > In fact, Michael and I have both been accused of the former, I happen to know, but not generally of the later. > > :-) > > However, there is also something to be said for honesty and I am afraid to say that the opinion you express is more commonly held than I would like to think, certainly, it doesn?t sit too far below the surface of many of the so called neuro-morphic models you see floating around, and I can tell you because I was there, that it was pretty apparent in the early days of the Neural Networks business as well. Perhaps worth noting at the same time that some ?notables? in that effort, John Hopfield for example, didn?t share that point of view, neither as far as I can tell did Carver Mead or Richard Feynman for that matter. Collectively the course they taught at Caltech in the early 80s on how the heck to figure out the brain was responsible for Caltech deciding to start the first computational biology graduate program (the CNS program). Those guys were smart enough or knew enough, or both, not to dismiss structures evolved over millions of years, under harsh and unforgiving circumstances that did remarkable things. > > My advice is that nobody else should either. > > Jim Bower > > > > On Mar 19, 2014, at 6:13 PM, Brian J Mingus wrote: > >> The hippocampus and cerebellum might be necessary variance. Data from strokes and lesion studies suggest that they are not fully necessary, however. Also, they might be local minima in the design space, and we might be able to replace them with something simpler before we figure out exactly how they work, by first identifying what it is that they do and then inventing something better. >> >> Brian >> >> >> On Wed, Mar 19, 2014 at 2:27 PM, Michael Arbib wrote: >> Ignoring the gross differences in circuitry between hippocampus and cerebellum, etc., is not erring on the side of simplicity, it is erring, period. Have you actually looked at a Cajal/Sxentagothai-style drawing of their circuitry? >> >> >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >>> Hi Jim, >>> >>> Focusing too much on the details is risky in and of itself. Optimal compression requires a balance, and we can't compute what that balance is (all models are wrong). One thing we can say for sure is that we should err on the side of simplicity, and adding detail to theories before simpler explanations have failed is not Ockham's heuristic. That said it's still in the space of a Big Data fuzzy science approach, where we throw as much data from as many levels of analysis as we can come up with into a big pot and then construct a theory. The thing to keep in mind is that when we start pruning this model most of the details are going to disappear, because almost all of them are irrelevant. Indeed, the size of the description that includes all the details is almost infinite, whereas the length of the description that explains almost all the variance is extremely short, especially in comparison. This is why Ockham's razor is a good heuristic. It helps prevent us from wasting time on unnecessary details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. >>> >>> > On 3/14/14 3:40 PM, Michael Arbib wrote: >>> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >>> >>> The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). >>> >> >>> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very different from each other. >> > -------------- next part -------------- An HTML attachment was scrubbed... URL: From g.goodhill at uq.edu.au Wed Mar 19 21:30:47 2014 From: g.goodhill at uq.edu.au (Geoffrey Goodhill) Date: Thu, 20 Mar 2014 01:30:47 +0000 Subject: Connectionists: how the brain works? In-Reply-To: <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> Message-ID: Hi All, A great example of successful Ockham-inspired biology is Alan Turing's model for pattern formation (spots, stripes etc) in embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical mechanism for how inhomogeneous spatial patterns can arise in a biological system from a spatially homogeneous starting point, based on the diffusion of morphogens. The paper begins: "In this section a mathematical model of the growing embryo will be described. This model will be a simplification and an idealization, and consequently a falsification. It is to be hoped that the features retained for discussion are those of greatest importance in the present state of knowledge." The paper remained virtually uncited for its first 20 years following publication, but since then has amassed 8000 citations (Google Scholar). The subsequent discovery of huge quantities of molecular detail in biological pattern formation have only reinforced the importance of this relatively simple model, not because it explains every system, but because the overarching concepts it introduced have proved to be so fertile. Cheers, Geoff On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: > Ignoring the gross differences in circuitry between hippocampus and cerebellum, etc., is not erring on the side of simplicity, it is erring, period. Have you actually looked at a Cajal/Sxentagothai-style drawing of their circuitry? > > At 01:07 PM 3/19/2014, Brian J Mingus wrote: >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. Optimal compression requires a balance, and we can't compute what that balance is (all models are wrong). One thing we can say for sure is that we should err on the side of simplicity, and adding detail to theories before simpler explanations have failed is not Ockham's heuristic. That said it's still in the space of a Big Data fuzzy science approach, where we throw as much data from as many levels of analysis as we can come up with into a big pot and then construct a theory. The thing to keep in mind is that when we start pruning this model most of the details are going to disappear, because almost all of them are irrelevant. Indeed, the size of the description that includes all the details is almost infinite, whereas the length of the description that explains almost all the variance is extremely short, especially in comparison. This is why Ockham's razor is a good heuristic. It helps prevent us from wasting time on unnecessary details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. >> >> > On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >>> The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). >> >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very different from each other. From brian.mingus at colorado.edu Wed Mar 19 21:29:26 2014 From: brian.mingus at colorado.edu (Brian J Mingus) Date: Wed, 19 Mar 2014 19:29:26 -0600 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> Message-ID: The second episode of the new Cosmos provides an excellent example of how evolution gets stuck in local minima that it can't climb out of. - Brian On Mar 19, 2014 6:00 PM, "james bower" wrote: > ah, someone once said (perhaps it was me) that arrogance and ignorance are > a particularly dangerous combination in a scientist - not that there are > not am absence of examples. > > In fact, Michael and I have both been accused of the former, I happen to > know, but not generally of the later. > > :-) > > However, there is also something to be said for honesty and I am afraid to > say that the opinion you express is more commonly held than I would like to > think, certainly, it doesn't sit too far below the surface of many of the > so called neuro-morphic models you see floating around, and I can tell you > because I was there, that it was pretty apparent in the early days of the > Neural Networks business as well. Perhaps worth noting at the same time > that some 'notables' in that effort, John Hopfield for example, didn't > share that point of view, neither as far as I can tell did Carver Mead or > Richard Feynman for that matter. Collectively the course they taught at > Caltech in the early 80s on how the heck to figure out the brain was > responsible for Caltech deciding to start the first computational biology > graduate program (the CNS program). Those guys were smart enough or knew > enough, or both, not to dismiss structures evolved over millions of years, > under harsh and unforgiving circumstances that did remarkable things. > > My advice is that nobody else should either. > > Jim Bower > > > > On Mar 19, 2014, at 6:13 PM, Brian J Mingus > wrote: > > The hippocampus and cerebellum might be necessary variance. Data from > strokes and lesion studies suggest that they are not fully necessary, > however. Also, they might be local minima in the design space, and we might > be able to replace them with something simpler before we figure out exactly > how they work, by first identifying what it is that they do and then > inventing something better. > > Brian > > > On Wed, Mar 19, 2014 at 2:27 PM, Michael Arbib wrote: > >> Ignoring the gross differences in circuitry between hippocampus and >> cerebellum, etc., is not erring on the side of simplicity, it is erring, >> period. Have you actually looked at a Cajal/Sxentagothai-style drawing of >> their circuitry? >> >> >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >> >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. Optimal >> compression requires a balance, and we can't compute what that balance is >> (all models are wrong). One thing we can say for sure is that we should err >> on the side of simplicity, and adding detail to theories before simpler >> explanations have failed is not Ockham's heuristic. That said it's still in >> the space of a Big Data fuzzy science approach, where we throw as much data >> from as many levels of analysis as we can come up with into a big pot and >> then construct a theory. The thing to keep in mind is that when we start >> pruning this model most of the details are going to disappear, because >> almost all of them are irrelevant. Indeed, the size of the description that >> includes all the details is almost infinite, whereas the length of the >> description that explains almost all the variance is extremely short, >> especially in comparison. This is why Ockham's razor is a good heuristic. >> It helps prevent us from wasting time on unnecessary details by suggesting >> that we only inquire as to the details once our existing simpler theory has >> failed to work. >> >> > On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >>> The brain uses a single architecture to do all brain functions we are >> aware of! It uses the same architecture to do vision, audition, motor, >> reasoning, decision making, motivation (including pain avoidance and >> pleasure seeking, novelty seeking, higher emotion, etc.). >> >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were >> very different from each other. >> >> > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From bower at uthscsa.edu Wed Mar 19 22:33:31 2014 From: bower at uthscsa.edu (james bower) Date: Wed, 19 Mar 2014 21:33:31 -0500 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> Message-ID: <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> Geoffrey, Nice addition to the discussion actually introducing an interesting angle on the question of brain organization (see below) As you note, reaction diffusion mechanisms and modeling have been quite successful in replicating patterns seen in biology - especially interesting I think is the modeling of patterns in slime molds, but also for very general pattern formation in embryology. However, more and more detailed analysis of what is diffusing, what is sensing what is diffusing, and what is reacting to substances once sensed ? all linked to complex patterns of gene regulation and expression have made it clear that actual embryological development is much much more complex, as Turing himself clearly anticipated, as the quote you cite pretty clearly indicates. Clearly a smart guy. But, I don?t actually think that this is an application of Ochham?s razor although it might appear to be after the fact. Just as Hodgkin and Huxley were not applying it either in their model of the action potential. Turing apparently guessed (based on a lot of work at the time on pattern formation with reaction diffusion) that such a mechanism might provide the natural basis for what embryos do. Thus, just like for Hodgkin and Huxley, his model resulted from a bio-physical insight, not an explicit attempt to build a stripped down model for its own sake. I seriously doubt that Turning would have claimed that he, or his models could more effectively do what biology actually does in forming an embrio, or substitute for the actual process. However, I think there is another interesting connection here to the discussion on modeling the brain. Almost certainly communication and organizational systems in early living beings were reaction diffusion based. This is still a dominant effect for many ?sensing? in small organisms. Perhaps, therefore, one can look at nervous systems as structures specifically developed to supersede reaction diffusion mechanisms, thus superseding this very ?natural? but complexity limited type of communication and organization. What this means, I believe, is that a simplified or abstracted physical or mathematical model of the brain explicitly violates the evolutionary pressures responsible for its structure. Its where the wires go, what the wires do, and what the receiving neuron does with the information that forms the basis for neural computation, multiplied by a very large number. And that is dependent on the actual physical structure of those elements. One more point about smart guys, as a young computational neurobiologist I questioned how insightful John von Neumann actually was because I was constantly hearing about a lecture he wrote (but didn?t give) at Yale suggesting that dendrites and neurons might be digital ( John von Neumann?s The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) Very clearly a not very insightful idea for a supposedly smart guy. It wasn?t until a few years later, when I actually read the lecture - that I found out that he ends by stating that this idea is almost certainly wrong, given the likely nonlinearities in neuronal dendrites. So von Neumann didn?t lack insight, the people who quoted him did. It is a remarkable fact that more than 60 years later, the majority of models of so called neurons built by engineers AND neurobiologists don?t consider these nonlinearities. The point being the same point, to the Hopfield, Mead, Feynman list, we can now add Turing and von Neumann as suspecting that for understanding, biology and the nervous system must be dealt with in their full complexity. But thanks for the example from Turing - always nice to consider actual examples. :-) Jim On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill wrote: > Hi All, > > A great example of successful Ockham-inspired biology is Alan Turing's model for pattern formation (spots, stripes etc) in embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical mechanism for how inhomogeneous spatial patterns can arise in a biological system from a spatially homogeneous starting point, based on the diffusion of morphogens. The paper begins: > > "In this section a mathematical model of the growing embryo will be described. This model will be a simplification and an idealization, and consequently a falsification. It is to be hoped that the features retained for discussion are those of greatest importance in the present state of knowledge." > > The paper remained virtually uncited for its first 20 years following publication, but since then has amassed 8000 citations (Google Scholar). The subsequent discovery of huge quantities of molecular detail in biological pattern formation have only reinforced the importance of this relatively simple model, not because it explains every system, but because the overarching concepts it introduced have proved to be so fertile. > > Cheers, > > Geoff > > > On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: > >> Ignoring the gross differences in circuitry between hippocampus and cerebellum, etc., is not erring on the side of simplicity, it is erring, period. Have you actually looked at a Cajal/Sxentagothai-style drawing of their circuitry? >> >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >>> Hi Jim, >>> >>> Focusing too much on the details is risky in and of itself. Optimal compression requires a balance, and we can't compute what that balance is (all models are wrong). One thing we can say for sure is that we should err on the side of simplicity, and adding detail to theories before simpler explanations have failed is not Ockham's heuristic. That said it's still in the space of a Big Data fuzzy science approach, where we throw as much data from as many levels of analysis as we can come up with into a big pot and then construct a theory. The thing to keep in mind is that when we start pruning this model most of the details are going to disappear, because almost all of them are irrelevant. Indeed, the size of the description that includes all the details is almost infinite, whereas the length of the description that explains almost all the variance is extremely short, especially in comparison. This is why Ockham's razor is a good heuristic. It helps prevent us from wasting time on unnecessary details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. >>> >>>> On 3/14/14 3:40 PM, Michael Arbib wrote: >>>>> At 11:17 AM 3/14/2014, Juyang Weng wrote: >>>>>> The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). >>>>> >>>>> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very different from each other. > -------------- next part -------------- An HTML attachment was scrubbed... URL: From levine at uta.edu Wed Mar 19 23:36:21 2014 From: levine at uta.edu (Levine, Daniel S) Date: Wed, 19 Mar 2014 22:36:21 -0500 Subject: Connectionists: how the brain works? In-Reply-To: <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> , <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> Message-ID: <581625BB6C84AB4BBA1C969C69E269EC025257260DE0@MAVMAIL2.uta.edu> Jim et al., To pioneers who understood more about complexity than we often give them credit for I'd add Warren McCulloch. McCulloch and Pitts are clearly well known for their theorem about networks of all-or-none neurons. And yet in about 1969 just before his death I met McCulloch as a graduate student who was just beginning to migrate from pure math into neural modeling. I asked him what area of math I should study in order to be an effective modeler, and he advised me to read a book by a Russian named Minorsky on nonlinear oscillations. Best, Dan Levine ________________________________ From: Connectionists [connectionists-bounces at mailman.srv.cs.cmu.edu] On Behalf Of james bower [bower at uthscsa.edu] Sent: Wednesday, March 19, 2014 9:33 PM To: Geoffrey Goodhill Cc: connectionists at mailman.srv.cs.cmu.edu Subject: Re: Connectionists: how the brain works? Geoffrey, Nice addition to the discussion actually introducing an interesting angle on the question of brain organization (see below) As you note, reaction diffusion mechanisms and modeling have been quite successful in replicating patterns seen in biology - especially interesting I think is the modeling of patterns in slime molds, but also for very general pattern formation in embryology. However, more and more detailed analysis of what is diffusing, what is sensing what is diffusing, and what is reacting to substances once sensed ? all linked to complex patterns of gene regulation and expression have made it clear that actual embryological development is much much more complex, as Turing himself clearly anticipated, as the quote you cite pretty clearly indicates. Clearly a smart guy. But, I don?t actually think that this is an application of Ochham?s razor although it might appear to be after the fact. Just as Hodgkin and Huxley were not applying it either in their model of the action potential. Turing apparently guessed (based on a lot of work at the time on pattern formation with reaction diffusion) that such a mechanism might provide the natural basis for what embryos do. Thus, just like for Hodgkin and Huxley, his model resulted from a bio-physical insight, not an explicit attempt to build a stripped down model for its own sake. I seriously doubt that Turning would have claimed that he, or his models could more effectively do what biology actually does in forming an embrio, or substitute for the actual process. However, I think there is another interesting connection here to the discussion on modeling the brain. Almost certainly communication and organizational systems in early living beings were reaction diffusion based. This is still a dominant effect for many ?sensing? in small organisms. Perhaps, therefore, one can look at nervous systems as structures specifically developed to supersede reaction diffusion mechanisms, thus superseding this very ?natural? but complexity limited type of communication and organization. What this means, I believe, is that a simplified or abstracted physical or mathematical model of the brain explicitly violates the evolutionary pressures responsible for its structure. Its where the wires go, what the wires do, and what the receiving neuron does with the information that forms the basis for neural computation, multiplied by a very large number. And that is dependent on the actual physical structure of those elements. One more point about smart guys, as a young computational neurobiologist I questioned how insightful John von Neumann actually was because I was constantly hearing about a lecture he wrote (but didn?t give) at Yale suggesting that dendrites and neurons might be digital ( John von Neumann?s The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) Very clearly a not very insightful idea for a supposedly smart guy. It wasn?t until a few years later, when I actually read the lecture - that I found out that he ends by stating that this idea is almost certainly wrong, given the likely nonlinearities in neuronal dendrites. So von Neumann didn?t lack insight, the people who quoted him did. It is a remarkable fact that more than 60 years later, the majority of models of so called neurons built by engineers AND neurobiologists don?t consider these nonlinearities. The point being the same point, to the Hopfield, Mead, Feynman list, we can now add Turing and von Neumann as suspecting that for understanding, biology and the nervous system must be dealt with in their full complexity. But thanks for the example from Turing - always nice to consider actual examples. :-) Jim On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill > wrote: Hi All, A great example of successful Ockham-inspired biology is Alan Turing's model for pattern formation (spots, stripes etc) in embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical mechanism for how inhomogeneous spatial patterns can arise in a biological system from a spatially homogeneous starting point, based on the diffusion of morphogens. The paper begins: "In this section a mathematical model of the growing embryo will be described. This model will be a simplification and an idealization, and consequently a falsification. It is to be hoped that the features retained for discussion are those of greatest importance in the present state of knowledge." The paper remained virtually uncited for its first 20 years following publication, but since then has amassed 8000 citations (Google Scholar). The subsequent discovery of huge quantities of molecular detail in biological pattern formation have only reinforced the importance of this relatively simple model, not because it explains every system, but because the overarching concepts it introduced have proved to be so fertile. Cheers, Geoff On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: Ignoring the gross differences in circuitry between hippocampus and cerebellum, etc., is not erring on the side of simplicity, it is erring, period. Have you actually looked at a Cajal/Sxentagothai-style drawing of their circuitry? At 01:07 PM 3/19/2014, Brian J Mingus wrote: Hi Jim, Focusing too much on the details is risky in and of itself. Optimal compression requires a balance, and we can't compute what that balance is (all models are wrong). One thing we can say for sure is that we should err on the side of simplicity, and adding detail to theories before simpler explanations have failed is not Ockham's heuristic. That said it's still in the space of a Big Data fuzzy science approach, where we throw as much data from as many levels of analysis as we can come up with into a big pot and then construct a theory. The thing to keep in mind is that when we start pruning this model most of the details are going to disappear, because almost all of them are irrelevant. Indeed, the size of the description that includes all the details is almost infinite, whereas the length of the description that explains almost all the variance is extremely short, especially in comparison. This is why Ockham's razor is a good heuristic. It helps prevent us from wasting time on unnecessary details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. On 3/14/14 3:40 PM, Michael Arbib wrote: At 11:17 AM 3/14/2014, Juyang Weng wrote: The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very different from each other. -------------- next part -------------- An HTML attachment was scrubbed... URL: From brian.mingus at colorado.edu Wed Mar 19 23:53:04 2014 From: brian.mingus at colorado.edu (Brian J Mingus) Date: Wed, 19 Mar 2014 21:53:04 -0600 Subject: Connectionists: how the brain works? In-Reply-To: <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> Message-ID: Jim, I feel like something is missing here. The Brain is information. Knowing its first principal component would be a *huge* advance for neuroscience. The principle goal of the Ockham gradient is to first identify this component. The theoretical basis for doing this is quite solid - I mean it's basically just hill climbing and it's honestly quite hard to reasonably dispute. Disputing it on the grounds of specific edge cases strikes me as confirmation bias. *If we can create a mean field theory model of the brain that reinvents consciousness philosophy, our principal work is done, even though we didn't include anything about Purkinje cells!* Since you mentioned you appreciate my honesty, I hope you don't mind a little more. :) The graident of abstract to specific (or, in the case of this thread, genotype to phenotype) is a very principled and good one. The actual gradient society is following - throw in in all the data from all the approaches into a pot and *then* do a PCA - is also going to work. It's just an inefficient waste of time when we could be taking a much more principled and effective approach. Brian On Wed, Mar 19, 2014 at 8:33 PM, james bower wrote: > Geoffrey, > > Nice addition to the discussion actually introducing an interesting angle > on the question of brain organization (see below) As you note, reaction > diffusion mechanisms and modeling have been quite successful in replicating > patterns seen in biology - especially interesting I think is the modeling > of patterns in slime molds, but also for very general pattern formation in > embryology. However, more and more detailed analysis of what is diffusing, > what is sensing what is diffusing, and what is reacting to substances once > sensed -- all linked to complex patterns of gene regulation and expression > have made it clear that actual embryological development is much much more > complex, as Turing himself clearly anticipated, as the quote you cite > pretty clearly indicates. Clearly a smart guy. But, I don't actually > think that this is an application of Ochham's razor although it might > appear to be after the fact. Just as Hodgkin and Huxley were not applying > it either in their model of the action potential. Turing apparently > guessed (based on a lot of work at the time on pattern formation with > reaction diffusion) that such a mechanism might provide the natural basis > for what embryos do. Thus, just like for Hodgkin and Huxley, his model > resulted from a bio-physical insight, not an explicit attempt to build a > stripped down model for its own sake. I seriously doubt that Turning > would have claimed that he, or his models could more effectively do what > biology actually does in forming an embrio, or substitute for the actual > process. > > However, I think there is another interesting connection here to the > discussion on modeling the brain. Almost certainly communication and > organizational systems in early living beings were reaction diffusion > based. This is still a dominant effect for many 'sensing' in small > organisms. Perhaps, therefore, one can look at nervous systems as > structures specifically developed to supersede reaction diffusion > mechanisms, thus superseding this very 'natural' but complexity limited > type of communication and organization. What this means, I believe, is > that a simplified or abstracted physical or mathematical model of the brain > explicitly violates the evolutionary pressures responsible for its > structure. Its where the wires go, what the wires do, and what the > receiving neuron does with the information that forms the basis for neural > computation, multiplied by a very large number. And that is dependent on > the actual physical structure of those elements. > > One more point about smart guys, as a young computational neurobiologist > I questioned how insightful John von Neumann actually was because I was > constantly hearing about a lecture he wrote (but didn't give) at Yale > suggesting that dendrites and neurons might be digital ( John von > Neumann's *The Computer and the Brain*. (New Haven/London: Yale Univesity > Press, 1958.) Very clearly a not very insightful idea for a supposedly > smart guy. It wasn't until a few years later, when I actually read the > lecture - that I found out that he ends by stating that this idea is almost > certainly wrong, given the likely nonlinearities in neuronal dendrites. So > von Neumann didn't lack insight, the people who quoted him did. It is a > remarkable fact that more than 60 years later, the majority of models of so > called neurons built by engineers AND neurobiologists don't consider these > nonlinearities. The point being the same point, to the Hopfield, Mead, > Feynman list, we can now add Turing and von Neumann as suspecting that for > understanding, biology and the nervous system must be dealt with in their > full complexity. > > But thanks for the example from Turing - always nice to consider actual > examples. :-) > > Jim > > > > > > On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill > wrote: > > Hi All, > > A great example of successful Ockham-inspired biology is Alan Turing's > model for pattern formation (spots, stripes etc) in embryology (The > chemical basis of morphogenesis, Phil Trans Roy Soc, 1953). Turing > introduced a physical mechanism for how inhomogeneous spatial patterns can > arise in a biological system from a spatially homogeneous starting point, > based on the diffusion of morphogens. The paper begins: > > "In this section a mathematical model of the growing embryo will be > described. This model will be a simplification and an idealization, and > consequently a falsification. It is to be hoped that the features retained > for discussion are those of greatest importance in the present state of > knowledge." > > The paper remained virtually uncited for its first 20 years following > publication, but since then has amassed 8000 citations (Google Scholar). > The subsequent discovery of huge quantities of molecular detail in > biological pattern formation have only reinforced the importance of this > relatively simple model, not because it explains every system, but because > the overarching concepts it introduced have proved to be so fertile. > > Cheers, > > Geoff > > > On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: > > Ignoring the gross differences in circuitry between hippocampus and > cerebellum, etc., is not erring on the side of simplicity, it is erring, > period. Have you actually looked at a Cajal/Sxentagothai-style drawing of > their circuitry? > > At 01:07 PM 3/19/2014, Brian J Mingus wrote: > > Hi Jim, > > Focusing too much on the details is risky in and of itself. Optimal > compression requires a balance, and we can't compute what that balance is > (all models are wrong). One thing we can say for sure is that we should err > on the side of simplicity, and adding detail to theories before simpler > explanations have failed is not Ockham's heuristic. That said it's still in > the space of a Big Data fuzzy science approach, where we throw as much data > from as many levels of analysis as we can come up with into a big pot and > then construct a theory. The thing to keep in mind is that when we start > pruning this model most of the details are going to disappear, because > almost all of them are irrelevant. Indeed, the size of the description that > includes all the details is almost infinite, whereas the length of the > description that explains almost all the variance is extremely short, > especially in comparison. This is why Ockham's razor is a good heuristic. > It helps prevent us from wasting time on unnecessary details by suggesting > that we only inquire as to the details once our existing simpler theory has > failed to work. > > On 3/14/14 3:40 PM, Michael Arbib wrote: > > At 11:17 AM 3/14/2014, Juyang Weng wrote: > > The brain uses a single architecture to do all brain functions we are > aware of! It uses the same architecture to do vision, audition, motor, > reasoning, decision making, motivation (including pain avoidance and > pleasure seeking, novelty seeking, higher emotion, etc.). > > > Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were > very different from each other. > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From marcel.van.gerven at gmail.com Thu Mar 20 03:46:44 2014 From: marcel.van.gerven at gmail.com (Marcel van Gerven) Date: Thu, 20 Mar 2014 08:46:44 +0100 Subject: Connectionists: Radboud Summer School on Neural Metrics Message-ID: Radboud Summer School Neural metrics: Quantitative analysis of neural organisation and function The Donders Institute for Brain, Cognition and Behaviour is organizing a summer school on neural metrics where the aim is to get participants acquainted with the quantitative analysis of neural organisation and function. We have compiled an exciting program with an excellent set of national and international speakers. Topics range from the analysis of single-neuron responses to the analysis of macroscopic brain networks. The course is designed for PhD students and starting postdoctoral researchers working at the interface between cognitive neuroscience and the application of advanced methods. Please consult the Radboud Summer School website for details on the programme, social events and registration. Further details for the Neural Metrics Summer School can be found below and on the website. Note that the early bird deadline is April 1st. Dates Monday 11 August - Friday 15 August 2014 (one week) Course leaders Dr. M. (Marcel) van Gerven, Assisstant Professor Prof. T. (Tansu) Celikel, Professor Neurophysiology Donders Institute for Brain, Cognition and Behaviour Entry level PhD students in the field of Neuroscience with an MSc in Biology, Computer Science, Psychology, Physics, Al or similar subject For whom is this course designed This course was developed for PhD students (10 local + 20 international) and early postdoctoral researchers working at the interface between cognitive neuroscience and the application of advanced methods. This includes cognitive neuroscientists and researchers with practical experience. Admission requirements As part of the admission procedure, we ask you to send us your CV and a motivation letter in which you explain your interest for our course. Course fee ?400 The course fee includes the registration fee, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities. Accommodation is available for the course participants. For details please see http://www.ru.nl/radboudsummerschool/practical-matters/housing/ Discounts ? 10% discount for early bird applicants. The early bird deadline is 1 April 2014. ? 15% discount for students and PhD candidates from Radboud University and partner universities Course description The brain as a network In order to fully understand the neuronal substrate of human cognition, we need to start viewing the working brain as a network.Many neurological and mental disorders are viewed as the result of a malfunction at the network level. The growing interest in brain networks is complemented by rapid theoretical and technical developments. We will embrace these developments throughout the course to help us understand the human brain network. This will be done by examining the theoretical background and by learning the necessary measurement and data analysis techniques used to study brain connectivity. The topics will be structured according to micro, meso and macro-scale connectivity. Course contents Micro-scale connectivity involves neuron communication at the cellular and synaptic level; mesa-scale connectivity addresses communication between brain regions; and macro-scale connectivity explores the structure and dynamics of large brain networks. We will strive to include components of electrophysiology, anatomy, functional blood flow measures, computational modelling and advanced data analysis at each level. While doing so, we will focus on both the animal and human brain. The course consists of lectures and computer exercises, supplemented with in-depth discussions. Lecture slides and exercises will also be distributed among the participants. We will ensure active participation in the following way: ? In the hands-on sessions you will work together in small groups (2-3 people). These sessions will include exercises aimed at promoting discussions and encouraging you to reflect on the core theoretical issues. Learning outcomes 1. Understand new techniques and approaches in the field of neuroscience networks 2. Understand the basics of new connectivity analysis tools 3. Understand new theories and computational modelling approaches 4. Identify the appropriate research methodology for answering specific research questions on brain connectivity 5. Improve your communication skills and develop research questions in a group setting Number of ECTS credits 2 ECTS credits Registration For registration, please consult the website. More information radboudsummerschool at ru.nl -------------- next part -------------- An HTML attachment was scrubbed... URL: From lorincz at inf.elte.hu Thu Mar 20 01:37:11 2014 From: lorincz at inf.elte.hu (Andras Lorincz) Date: Thu, 20 Mar 2014 05:37:11 +0000 Subject: Connectionists: how the brain works? In-Reply-To: <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> , <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> Message-ID: <82a4a4bc8bbe49a9906d6dbac9ba314d@DB4PR06MB173.eurprd06.prod.outlook.com> Ockham works here via compressing both the algorithm and the structure. Compressing the structure to stem cells means that the algorithm should describe the development, the working, and the time dependent structure of the brain. Not compressing the description of the structure of the evolved brain is a different problem since it saves the need for the description of the development, but the working. Understanding the structure and the working of one part of the brain requires the description of its communication that increases the complexity of the description. By the way, this holds for the whole brain, so we might have to include the body at least; a structural minimist may wish to start from the genetic code, use that hint and unfold the already compressed description. There are (many and different) todos 'outside' ... Andras . ________________________________ From: Connectionists on behalf of james bower Sent: Thursday, March 20, 2014 3:33 AM To: Geoffrey Goodhill Cc: connectionists at mailman.srv.cs.cmu.edu Subject: Re: Connectionists: how the brain works? Geoffrey, Nice addition to the discussion actually introducing an interesting angle on the question of brain organization (see below) As you note, reaction diffusion mechanisms and modeling have been quite successful in replicating patterns seen in biology - especially interesting I think is the modeling of patterns in slime molds, but also for very general pattern formation in embryology. However, more and more detailed analysis of what is diffusing, what is sensing what is diffusing, and what is reacting to substances once sensed ? all linked to complex patterns of gene regulation and expression have made it clear that actual embryological development is much much more complex, as Turing himself clearly anticipated, as the quote you cite pretty clearly indicates. Clearly a smart guy. But, I don?t actually think that this is an application of Ochham?s razor although it might appear to be after the fact. Just as Hodgkin and Huxley were not applying it either in their model of the action potential. Turing apparently guessed (based on a lot of work at the time on pattern formation with reaction diffusion) that such a mechanism might provide the natural basis for what embryos do. Thus, just like for Hodgkin and Huxley, his model resulted from a bio-physical insight, not an explicit attempt to build a stripped down model for its own sake. I seriously doubt that Turning would have claimed that he, or his models could more effectively do what biology actually does in forming an embrio, or substitute for the actual process. However, I think there is another interesting connection here to the discussion on modeling the brain. Almost certainly communication and organizational systems in early living beings were reaction diffusion based. This is still a dominant effect for many ?sensing? in small organisms. Perhaps, therefore, one can look at nervous systems as structures specifically developed to supersede reaction diffusion mechanisms, thus superseding this very ?natural? but complexity limited type of communication and organization. What this means, I believe, is that a simplified or abstracted physical or mathematical model of the brain explicitly violates the evolutionary pressures responsible for its structure. Its where the wires go, what the wires do, and what the receiving neuron does with the information that forms the basis for neural computation, multiplied by a very large number. And that is dependent on the actual physical structure of those elements. One more point about smart guys, as a young computational neurobiologist I questioned how insightful John von Neumann actually was because I was constantly hearing about a lecture he wrote (but didn?t give) at Yale suggesting that dendrites and neurons might be digital ( John von Neumann?s The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) Very clearly a not very insightful idea for a supposedly smart guy. It wasn?t until a few years later, when I actually read the lecture - that I found out that he ends by stating that this idea is almost certainly wrong, given the likely nonlinearities in neuronal dendrites. So von Neumann didn?t lack insight, the people who quoted him did. It is a remarkable fact that more than 60 years later, the majority of models of so called neurons built by engineers AND neurobiologists don?t consider these nonlinearities. The point being the same point, to the Hopfield, Mead, Feynman list, we can now add Turing and von Neumann as suspecting that for understanding, biology and the nervous system must be dealt with in their full complexity. But thanks for the example from Turing - always nice to consider actual examples. :-) Jim On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill > wrote: Hi All, A great example of successful Ockham-inspired biology is Alan Turing's model for pattern formation (spots, stripes etc) in embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical mechanism for how inhomogeneous spatial patterns can arise in a biological system from a spatially homogeneous starting point, based on the diffusion of morphogens. The paper begins: "In this section a mathematical model of the growing embryo will be described. This model will be a simplification and an idealization, and consequently a falsification. It is to be hoped that the features retained for discussion are those of greatest importance in the present state of knowledge." The paper remained virtually uncited for its first 20 years following publication, but since then has amassed 8000 citations (Google Scholar). The subsequent discovery of huge quantities of molecular detail in biological pattern formation have only reinforced the importance of this relatively simple model, not because it explains every system, but because the overarching concepts it introduced have proved to be so fertile. Cheers, Geoff On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: Ignoring the gross differences in circuitry between hippocampus and cerebellum, etc., is not erring on the side of simplicity, it is erring, period. Have you actually looked at a Cajal/Sxentagothai-style drawing of their circuitry? At 01:07 PM 3/19/2014, Brian J Mingus wrote: Hi Jim, Focusing too much on the details is risky in and of itself. Optimal compression requires a balance, and we can't compute what that balance is (all models are wrong). One thing we can say for sure is that we should err on the side of simplicity, and adding detail to theories before simpler explanations have failed is not Ockham's heuristic. That said it's still in the space of a Big Data fuzzy science approach, where we throw as much data from as many levels of analysis as we can come up with into a big pot and then construct a theory. The thing to keep in mind is that when we start pruning this model most of the details are going to disappear, because almost all of them are irrelevant. Indeed, the size of the description that includes all the details is almost infinite, whereas the length of the description that explains almost all the variance is extremely short, especially in comparison. This is why Ockham's razor is a good heuristic. It helps prevent us from wasting time on unnecessary details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. On 3/14/14 3:40 PM, Michael Arbib wrote: At 11:17 AM 3/14/2014, Juyang Weng wrote: The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very different from each other. -------------- next part -------------- An HTML attachment was scrubbed... URL: From achler at gmail.com Thu Mar 20 10:41:15 2014 From: achler at gmail.com (Tsvi Achler) Date: Thu, 20 Mar 2014 07:41:15 -0700 Subject: Connectionists: how the brain works? In-Reply-To: <82a4a4bc8bbe49a9906d6dbac9ba314d@DB4PR06MB173.eurprd06.prod.outlook.com> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> <82a4a4bc8bbe49a9906d6dbac9ba314d@DB4PR06MB173.eurprd06.prod.outlook.com> Message-ID: I think an Ockham's razor principle can be used to find the most optimal algorithm if it is interpreted to mean the model with the least amount of free parameters that captures the most phenomena. http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.pdf -Tsvi On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz wrote: > Ockham works here via compressing both the algorithm and the structure. > Compressing the structure to stem cells means that the algorithm should > describe the development, the working, and the time dependent structure of > the brain. Not compressing the description of the structure of the evolved > brain is a different problem since it saves the need for the description of > the development, but the working. Understanding the structure and the > working of one part of the brain requires the description of its > communication that increases the complexity of the description. By the way, > this holds for the whole brain, so we might have to include the body at > least; a structural minimist may wish to start from the genetic code, use > that hint and unfold the already compressed description. There are (many and > different) todos 'outside' ... > > > Andras > > > > > . > > ________________________________ > From: Connectionists on > behalf of james bower > Sent: Thursday, March 20, 2014 3:33 AM > > To: Geoffrey Goodhill > Cc: connectionists at mailman.srv.cs.cmu.edu > Subject: Re: Connectionists: how the brain works? > > Geoffrey, > > Nice addition to the discussion actually introducing an interesting angle on > the question of brain organization (see below) As you note, reaction > diffusion mechanisms and modeling have been quite successful in replicating > patterns seen in biology - especially interesting I think is the modeling of > patterns in slime molds, but also for very general pattern formation in > embryology. However, more and more detailed analysis of what is diffusing, > what is sensing what is diffusing, and what is reacting to substances once > sensed -- all linked to complex patterns of gene regulation and expression > have made it clear that actual embryological development is much much more > complex, as Turing himself clearly anticipated, as the quote you cite pretty > clearly indicates. Clearly a smart guy. But, I don't actually think that > this is an application of Ochham's razor although it might appear to be > after the fact. Just as Hodgkin and Huxley were not applying it either in > their model of the action potential. Turing apparently guessed (based on a > lot of work at the time on pattern formation with reaction diffusion) that > such a mechanism might provide the natural basis for what embryos do. Thus, > just like for Hodgkin and Huxley, his model resulted from a bio-physical > insight, not an explicit attempt to build a stripped down model for its own > sake. I seriously doubt that Turning would have claimed that he, or his > models could more effectively do what biology actually does in forming an > embrio, or substitute for the actual process. > > However, I think there is another interesting connection here to the > discussion on modeling the brain. Almost certainly communication and > organizational systems in early living beings were reaction diffusion based. > This is still a dominant effect for many 'sensing' in small organisms. > Perhaps, therefore, one can look at nervous systems as structures > specifically developed to supersede reaction diffusion mechanisms, thus > superseding this very 'natural' but complexity limited type of communication > and organization. What this means, I believe, is that a simplified or > abstracted physical or mathematical model of the brain explicitly violates > the evolutionary pressures responsible for its structure. Its where the > wires go, what the wires do, and what the receiving neuron does with the > information that forms the basis for neural computation, multiplied by a > very large number. And that is dependent on the actual physical structure > of those elements. > > One more point about smart guys, as a young computational neurobiologist I > questioned how insightful John von Neumann actually was because I was > constantly hearing about a lecture he wrote (but didn't give) at Yale > suggesting that dendrites and neurons might be digital ( John von Neumann's > The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) > Very clearly a not very insightful idea for a supposedly smart guy. It > wasn't until a few years later, when I actually read the lecture - that I > found out that he ends by stating that this idea is almost certainly wrong, > given the likely nonlinearities in neuronal dendrites. So von Neumann > didn't lack insight, the people who quoted him did. It is a remarkable fact > that more than 60 years later, the majority of models of so called neurons > built by engineers AND neurobiologists don't consider these nonlinearities. > The point being the same point, to the Hopfield, Mead, Feynman list, we can > now add Turing and von Neumann as suspecting that for understanding, > biology and the nervous system must be dealt with in their full complexity. > > But thanks for the example from Turing - always nice to consider actual > examples. :-) > > Jim > > > > > > On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill wrote: > > Hi All, > > A great example of successful Ockham-inspired biology is Alan Turing's model > for pattern formation (spots, stripes etc) in embryology (The chemical basis > of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical > mechanism for how inhomogeneous spatial patterns can arise in a biological > system from a spatially homogeneous starting point, based on the diffusion > of morphogens. The paper begins: > > "In this section a mathematical model of the growing embryo will be > described. This model will be a simplification and an idealization, and > consequently a falsification. It is to be hoped that the features retained > for discussion are those of greatest importance in the present state of > knowledge." > > The paper remained virtually uncited for its first 20 years following > publication, but since then has amassed 8000 citations (Google Scholar). The > subsequent discovery of huge quantities of molecular detail in biological > pattern formation have only reinforced the importance of this relatively > simple model, not because it explains every system, but because the > overarching concepts it introduced have proved to be so fertile. > > Cheers, > > Geoff > > > On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: > > Ignoring the gross differences in circuitry between hippocampus and > cerebellum, etc., is not erring on the side of simplicity, it is erring, > period. Have you actually looked at a Cajal/Sxentagothai-style drawing of > their circuitry? > > At 01:07 PM 3/19/2014, Brian J Mingus wrote: > > Hi Jim, > > Focusing too much on the details is risky in and of itself. Optimal > compression requires a balance, and we can't compute what that balance is > (all models are wrong). One thing we can say for sure is that we should err > on the side of simplicity, and adding detail to theories before simpler > explanations have failed is not Ockham's heuristic. That said it's still in > the space of a Big Data fuzzy science approach, where we throw as much data > from as many levels of analysis as we can come up with into a big pot and > then construct a theory. The thing to keep in mind is that when we start > pruning this model most of the details are going to disappear, because > almost all of them are irrelevant. Indeed, the size of the description that > includes all the details is almost infinite, whereas the length of the > description that explains almost all the variance is extremely short, > especially in comparison. This is why Ockham's razor is a good heuristic. It > helps prevent us from wasting time on unnecessary details by suggesting that > we only inquire as to the details once our existing simpler theory has > failed to work. > > On 3/14/14 3:40 PM, Michael Arbib wrote: > > At 11:17 AM 3/14/2014, Juyang Weng wrote: > > The brain uses a single architecture to do all brain functions we are aware > of! It uses the same architecture to do vision, audition, motor, reasoning, > decision making, motivation (including pain avoidance and pleasure seeking, > novelty seeking, higher emotion, etc.). > > > Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very > different from each other. > > > From vcutsuridis at gmail.com Thu Mar 20 11:32:34 2014 From: vcutsuridis at gmail.com (Vassilis Cutsuridis) Date: Thu, 20 Mar 2014 17:32:34 +0200 Subject: Connectionists: Frontiers Research Topic: Memory Processes in Medial Temporal Lobe: Experimental, Theoretical and Computational Approaches Message-ID: Dear colleagues, we would like to inform you that our Research Topic organized with Frontiers in Neuroscience (Host Specialty: Frontiers in Systems Neuroscience) is still open and it will be accepting abstracts till *May 1st, 2014*. Our Research Topic is entitled: "Memory Processes in Medial Temporal Lobe: Experimental, Theoretical and Computational Approaches" Topic Editors: Motoharu Yoshida (University of Bochum) and Vassilis Cutsuridis (Foundation for Research and Technology - Hellas (FORTH)) Important deadlines ---------------------------- Abstract Submission Deadline: *May 1st, 2014* Article Submission Deadline: August 1st, 2014 Research Topic Description ---------------------------------------- The medial temporal lobe (MTL) includes the hippocampus, amygdala and parahippocampal regions, and is crucial for episodic and spatial memory. MTL memory function consists of distinct processes such as encoding, consolidation and retrieval. Encoding is the process by which perceived information is transformed into a memory trace. After encoding, memory traces are stabilized by consolidation. Memory retrieval (recall) refers to the process by which memory traces are reactivated to access information previously encoded and stored in the brain. Although underlying neural mechanisms supporting these distinct functional stages remain largely unknown, recent studies have indicated that distinct oscillatory dynamics, specific neuron types, synaptic plasticity and neuromodulation, play a central role. The theta rhythm is believed to be crucial in the encoding and retrieval of memories. Experimental and computational studies indicate that precise timing of principal cell firing in the hippocampus, relative to the theta rhythm, underlies encoding and retrieval processes. On the other hand, sharp-wave ripples have been implicated in the consolidation through the "replay" of memories in compressed time scales. The neural circuits and cell types supporting memory processes in the MTL areas have only recently been delineated using experimental approaches such as optogenetics, juxtacellular recordings and optical imaging. Principal (excitatory) cells are crucial for encoding, storing and retrieving memories at the cellular level, whereas inhibitory interneurons provide the temporal structures for orchestrating the activities of neuronal populations of principal cells by regulating synaptic integration and timing of action potential generation of principal cells as well as the generation and maintenance of network oscillations (rhythms). In addition, neuromodulators such as acetylcholine alter dynamical properties of neurons and synapses, and modulate oscillatory state and rules of synaptic plasticity and their levels might tune MTL to specific memory processes. The goal of the research topic is to offer a snapshot of the current stateof-the-art on how memories are encoded, consolidated, stored and retrieved in MTL structures. Particularly welcome will be studies (experimental or computational) focusing on the structure and function of neural circuits, their cellular components (principal cell and inhibitory interneurons), synaptic plasticity rules involved in these memory processes, network oscillations such as theta and sharp-wave ripples, and role of neuromodulators. Questions to be addressed: (1) Which areas or pathways within the MTL support encoding/consolidation/retrieval? (2) What neural activity defines specific memory processes? (3) What are the roles of neuromodulators in defining/switching these memory processes? (4) Could the role of synaptic plasticity be different in different memory processes? (5) What functional roles do the various inhibitory interneurons support during the encoding/consolidation/retrieval processes? About Frontiers Research Topics ------------------------------------------------ Frontiers Research Topics are designed to be an organized, encyclopedic coverage of a particular research area, and a forum for discussion and debate. Contributions can be of different article types (Original Research, Methods, Hypothesis & Theory, and others). Our Research Topic has a dedicated homepage on the Frontiers website, where contributing articles are accumulated and discussions can be easily held. Once all articles are published, the topic will be compiled into an e-book, which can be sent to foundations that fund your research, to journalists and press agencies, and to any number of other organizations. As the ultimate reference source from leading scientists, Frontiers Research Topic articles become highly cited. Frontiers is a Swiss-based, open access publisher. As such an article accepted for publication incurs a publishing fee, which varies depending on the article type. 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The details regarding this Research Topic for Frontiers in Systems Neuroscience can be found at the following URL: http://www.frontiersin.org/systems_neuroscience/researchtopics/memory_processes_in_medial_tem_1/2540 Should you choose to participate, please confirm by sending a quick email and then your abstract using the following link: http://www.frontiersin.org/submissioninfo Thanks in advance for your interest! Vassilis Cutsuridis and Motoharu Yoshida --- Vassilis Cutsuridis, PhD IMBB - FORTH Heraklion, Crete Greece -------------- next part -------------- An HTML attachment was scrubbed... URL: From ted.carnevale at yale.edu Thu Mar 20 10:53:58 2014 From: ted.carnevale at yale.edu (Ted Carnevale) Date: Thu, 20 Mar 2014 10:53:58 -0400 Subject: Connectionists: NSG provides free access to supercomputers for parallel simulations Message-ID: <532B0106.4030105@yale.edu> The NSF-supported Neuroscience Gateway (NSG) http://www.nsgportal.org allows computational neuroscientists to run parallel simulations, free of charge, on supercomputers using tools like GENESIS, NEURON, NEST, Brian, and PyNN. Other tools will be added based on users' requests. NSG provides a simple web-based interface that makes it quick and easy to create an account, upload model code, run simulations, and get back results. To get started, go to http://www.nsgportal.org , click on the "Go to the NSG Portal" button, and follow the instructions in the sentence: "New users who are interested in getting an account should fill out the form and email it to nsghelp at sdsc.edu" For any questions or suggestions related to the NSG portal, please contact us at nsghelp at sdsc.edu The NSG developers team From bower at uthscsa.edu Thu Mar 20 13:59:46 2014 From: bower at uthscsa.edu (james bower) Date: Thu, 20 Mar 2014 12:59:46 -0500 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> <82a4a4bc8bbe49a9906d6dbac9ba314d@DB4PR06MB173.eurprd06.prod.outlook.com> Message-ID: <7127C02E-FECA-4A67-A0E8-E2E5FE7D4926@uthscsa.edu> Interesting definition - just to note, we build realistic biological models with hundreds to thousands of parameters to model a neuron - however, they are not ?free?. in fact, it is easier to get a fake model of a neuron to behave like a neuron than it is to make one designed to replicate the anatomy and physiology. Something that many in the physics / engineering worlds don?t realize. So, in fact, by the definition you use, abstract neuronal models which have a smaller number of essentially completely free parameters can?t use Ockham?s shaving cream as cover. :-) Again, to return to my old analogy - Kepler was forced BY THE DATA to use ellipses for the orbits of the planets. It is pretty clear he would rather have not. Newton on the other hand clearly benefited from the fact that the moons orbit is essentially circular in his first calculation of the inverse square law. In both cases, however, unlike Ptolomy, they were constructing physically realistic models. Doing so in Biology also requires you deal with complexity you would rather not (as I keep saying ad naus?? ) Jim On Mar 20, 2014, at 9:41 AM, Tsvi Achler wrote: > I think an Ockham's razor principle can be used to find the most > optimal algorithm if it is interpreted to mean the model with the > least amount of free parameters that captures the most phenomena. > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.pdf > -Tsvi > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz wrote: >> Ockham works here via compressing both the algorithm and the structure. >> Compressing the structure to stem cells means that the algorithm should >> describe the development, the working, and the time dependent structure of >> the brain. Not compressing the description of the structure of the evolved >> brain is a different problem since it saves the need for the description of >> the development, but the working. Understanding the structure and the >> working of one part of the brain requires the description of its >> communication that increases the complexity of the description. By the way, >> this holds for the whole brain, so we might have to include the body at >> least; a structural minimist may wish to start from the genetic code, use >> that hint and unfold the already compressed description. There are (many and >> different) todos 'outside' ... >> >> >> Andras >> >> >> >> >> . >> >> ________________________________ >> From: Connectionists on >> behalf of james bower >> Sent: Thursday, March 20, 2014 3:33 AM >> >> To: Geoffrey Goodhill >> Cc: connectionists at mailman.srv.cs.cmu.edu >> Subject: Re: Connectionists: how the brain works? >> >> Geoffrey, >> >> Nice addition to the discussion actually introducing an interesting angle on >> the question of brain organization (see below) As you note, reaction >> diffusion mechanisms and modeling have been quite successful in replicating >> patterns seen in biology - especially interesting I think is the modeling of >> patterns in slime molds, but also for very general pattern formation in >> embryology. However, more and more detailed analysis of what is diffusing, >> what is sensing what is diffusing, and what is reacting to substances once >> sensed -- all linked to complex patterns of gene regulation and expression >> have made it clear that actual embryological development is much much more >> complex, as Turing himself clearly anticipated, as the quote you cite pretty >> clearly indicates. Clearly a smart guy. But, I don't actually think that >> this is an application of Ochham's razor although it might appear to be >> after the fact. Just as Hodgkin and Huxley were not applying it either in >> their model of the action potential. Turing apparently guessed (based on a >> lot of work at the time on pattern formation with reaction diffusion) that >> such a mechanism might provide the natural basis for what embryos do. Thus, >> just like for Hodgkin and Huxley, his model resulted from a bio-physical >> insight, not an explicit attempt to build a stripped down model for its own >> sake. I seriously doubt that Turning would have claimed that he, or his >> models could more effectively do what biology actually does in forming an >> embrio, or substitute for the actual process. >> >> However, I think there is another interesting connection here to the >> discussion on modeling the brain. Almost certainly communication and >> organizational systems in early living beings were reaction diffusion based. >> This is still a dominant effect for many 'sensing' in small organisms. >> Perhaps, therefore, one can look at nervous systems as structures >> specifically developed to supersede reaction diffusion mechanisms, thus >> superseding this very 'natural' but complexity limited type of communication >> and organization. What this means, I believe, is that a simplified or >> abstracted physical or mathematical model of the brain explicitly violates >> the evolutionary pressures responsible for its structure. Its where the >> wires go, what the wires do, and what the receiving neuron does with the >> information that forms the basis for neural computation, multiplied by a >> very large number. And that is dependent on the actual physical structure >> of those elements. >> >> One more point about smart guys, as a young computational neurobiologist I >> questioned how insightful John von Neumann actually was because I was >> constantly hearing about a lecture he wrote (but didn't give) at Yale >> suggesting that dendrites and neurons might be digital ( John von Neumann's >> The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) >> Very clearly a not very insightful idea for a supposedly smart guy. It >> wasn't until a few years later, when I actually read the lecture - that I >> found out that he ends by stating that this idea is almost certainly wrong, >> given the likely nonlinearities in neuronal dendrites. So von Neumann >> didn't lack insight, the people who quoted him did. It is a remarkable fact >> that more than 60 years later, the majority of models of so called neurons >> built by engineers AND neurobiologists don't consider these nonlinearities. >> The point being the same point, to the Hopfield, Mead, Feynman list, we can >> now add Turing and von Neumann as suspecting that for understanding, >> biology and the nervous system must be dealt with in their full complexity. >> >> But thanks for the example from Turing - always nice to consider actual >> examples. :-) >> >> Jim >> >> >> >> >> >> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill wrote: >> >> Hi All, >> >> A great example of successful Ockham-inspired biology is Alan Turing's model >> for pattern formation (spots, stripes etc) in embryology (The chemical basis >> of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical >> mechanism for how inhomogeneous spatial patterns can arise in a biological >> system from a spatially homogeneous starting point, based on the diffusion >> of morphogens. The paper begins: >> >> "In this section a mathematical model of the growing embryo will be >> described. This model will be a simplification and an idealization, and >> consequently a falsification. It is to be hoped that the features retained >> for discussion are those of greatest importance in the present state of >> knowledge." >> >> The paper remained virtually uncited for its first 20 years following >> publication, but since then has amassed 8000 citations (Google Scholar). The >> subsequent discovery of huge quantities of molecular detail in biological >> pattern formation have only reinforced the importance of this relatively >> simple model, not because it explains every system, but because the >> overarching concepts it introduced have proved to be so fertile. >> >> Cheers, >> >> Geoff >> >> >> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: >> >> Ignoring the gross differences in circuitry between hippocampus and >> cerebellum, etc., is not erring on the side of simplicity, it is erring, >> period. Have you actually looked at a Cajal/Sxentagothai-style drawing of >> their circuitry? >> >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >> >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. Optimal >> compression requires a balance, and we can't compute what that balance is >> (all models are wrong). One thing we can say for sure is that we should err >> on the side of simplicity, and adding detail to theories before simpler >> explanations have failed is not Ockham's heuristic. That said it's still in >> the space of a Big Data fuzzy science approach, where we throw as much data >> from as many levels of analysis as we can come up with into a big pot and >> then construct a theory. The thing to keep in mind is that when we start >> pruning this model most of the details are going to disappear, because >> almost all of them are irrelevant. Indeed, the size of the description that >> includes all the details is almost infinite, whereas the length of the >> description that explains almost all the variance is extremely short, >> especially in comparison. This is why Ockham's razor is a good heuristic. It >> helps prevent us from wasting time on unnecessary details by suggesting that >> we only inquire as to the details once our existing simpler theory has >> failed to work. >> >> On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >> The brain uses a single architecture to do all brain functions we are aware >> of! It uses the same architecture to do vision, audition, motor, reasoning, >> decision making, motivation (including pain avoidance and pleasure seeking, >> novelty seeking, higher emotion, etc.). >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very >> different from each other. >> >> >> From achler at gmail.com Thu Mar 20 15:00:02 2014 From: achler at gmail.com (Tsvi Achler) Date: Thu, 20 Mar 2014 12:00:02 -0700 Subject: Connectionists: how the brain works? In-Reply-To: <7127C02E-FECA-4A67-A0E8-E2E5FE7D4926@uthscsa.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> <82a4a4bc8bbe49a9906d6dbac9ba314d@DB4PR06MB173.eurprd06.prod.outlook.com> <7127C02E-FECA-4A67-A0E8-E2E5FE7D4926@uthscsa.edu> Message-ID: Basically it is a definition of over-fitting. The fact large that models may be needed to capture neuron components such proteins and so on do not change the fact that too many extraneous parameters can make the model precise but brittle. -Tsvi On Thu, Mar 20, 2014 at 10:59 AM, james bower wrote: > Interesting definition - > > just to note, we build realistic biological models with hundreds to > thousands of parameters to model a neuron - however, they are not "free". > > in fact, it is easier to get a fake model of a neuron to behave like a > neuron than it is to make one designed to replicate the anatomy and > physiology. Something that many in the physics / engineering worlds don't > realize. So, in fact, by the definition you use, abstract neuronal models > which have a smaller number of essentially completely free parameters can't > use Ockham's shaving cream as cover. :-) > > Again, to return to my old analogy - Kepler was forced BY THE DATA to use > ellipses for the orbits of the planets. It is pretty clear he would rather > have not. Newton on the other hand clearly benefited from the fact that > the moons orbit is essentially circular in his first calculation of the > inverse square law. In both cases, however, unlike Ptolomy, they were > constructing physically realistic models. Doing so in Biology also > requires you deal with complexity you would rather not (as I keep saying ad > naus...... ) > > Jim > > > On Mar 20, 2014, at 9:41 AM, Tsvi Achler wrote: > > > I think an Ockham's razor principle can be used to find the most > > optimal algorithm if it is interpreted to mean the model with the > > least amount of free parameters that captures the most phenomena. > > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.pdf > > -Tsvi > > > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz > wrote: > >> Ockham works here via compressing both the algorithm and the structure. > >> Compressing the structure to stem cells means that the algorithm should > >> describe the development, the working, and the time dependent structure > of > >> the brain. Not compressing the description of the structure of the > evolved > >> brain is a different problem since it saves the need for the > description of > >> the development, but the working. Understanding the structure and the > >> working of one part of the brain requires the description of its > >> communication that increases the complexity of the description. By the > way, > >> this holds for the whole brain, so we might have to include the body at > >> least; a structural minimist may wish to start from the genetic code, > use > >> that hint and unfold the already compressed description. There are > (many and > >> different) todos 'outside' ... > >> > >> > >> Andras > >> > >> > >> > >> > >> . > >> > >> ________________________________ > >> From: Connectionists on > >> behalf of james bower > >> Sent: Thursday, March 20, 2014 3:33 AM > >> > >> To: Geoffrey Goodhill > >> Cc: connectionists at mailman.srv.cs.cmu.edu > >> Subject: Re: Connectionists: how the brain works? > >> > >> Geoffrey, > >> > >> Nice addition to the discussion actually introducing an interesting > angle on > >> the question of brain organization (see below) As you note, reaction > >> diffusion mechanisms and modeling have been quite successful in > replicating > >> patterns seen in biology - especially interesting I think is the > modeling of > >> patterns in slime molds, but also for very general pattern formation in > >> embryology. However, more and more detailed analysis of what is > diffusing, > >> what is sensing what is diffusing, and what is reacting to substances > once > >> sensed -- all linked to complex patterns of gene regulation and > expression > >> have made it clear that actual embryological development is much much > more > >> complex, as Turing himself clearly anticipated, as the quote you cite > pretty > >> clearly indicates. Clearly a smart guy. But, I don't actually think > that > >> this is an application of Ochham's razor although it might appear to be > >> after the fact. Just as Hodgkin and Huxley were not applying it either > in > >> their model of the action potential. Turing apparently guessed (based > on a > >> lot of work at the time on pattern formation with reaction diffusion) > that > >> such a mechanism might provide the natural basis for what embryos do. > Thus, > >> just like for Hodgkin and Huxley, his model resulted from a bio-physical > >> insight, not an explicit attempt to build a stripped down model for its > own > >> sake. I seriously doubt that Turning would have claimed that he, or > his > >> models could more effectively do what biology actually does in forming > an > >> embrio, or substitute for the actual process. > >> > >> However, I think there is another interesting connection here to the > >> discussion on modeling the brain. Almost certainly communication and > >> organizational systems in early living beings were reaction diffusion > based. > >> This is still a dominant effect for many 'sensing' in small organisms. > >> Perhaps, therefore, one can look at nervous systems as structures > >> specifically developed to supersede reaction diffusion mechanisms, thus > >> superseding this very 'natural' but complexity limited type of > communication > >> and organization. What this means, I believe, is that a simplified or > >> abstracted physical or mathematical model of the brain explicitly > violates > >> the evolutionary pressures responsible for its structure. Its where the > >> wires go, what the wires do, and what the receiving neuron does with the > >> information that forms the basis for neural computation, multiplied by a > >> very large number. And that is dependent on the actual physical > structure > >> of those elements. > >> > >> One more point about smart guys, as a young computational > neurobiologist I > >> questioned how insightful John von Neumann actually was because I was > >> constantly hearing about a lecture he wrote (but didn't give) at Yale > >> suggesting that dendrites and neurons might be digital ( John von > Neumann's > >> The Computer and the Brain. (New Haven/London: Yale Univesity Press, > 1958.) > >> Very clearly a not very insightful idea for a supposedly smart guy. It > >> wasn't until a few years later, when I actually read the lecture - that > I > >> found out that he ends by stating that this idea is almost certainly > wrong, > >> given the likely nonlinearities in neuronal dendrites. So von Neumann > >> didn't lack insight, the people who quoted him did. It is a remarkable > fact > >> that more than 60 years later, the majority of models of so called > neurons > >> built by engineers AND neurobiologists don't consider these > nonlinearities. > >> The point being the same point, to the Hopfield, Mead, Feynman list, we > can > >> now add Turing and von Neumann as suspecting that for understanding, > >> biology and the nervous system must be dealt with in their full > complexity. > >> > >> But thanks for the example from Turing - always nice to consider actual > >> examples. :-) > >> > >> Jim > >> > >> > >> > >> > >> > >> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill > wrote: > >> > >> Hi All, > >> > >> A great example of successful Ockham-inspired biology is Alan Turing's > model > >> for pattern formation (spots, stripes etc) in embryology (The chemical > basis > >> of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a > physical > >> mechanism for how inhomogeneous spatial patterns can arise in a > biological > >> system from a spatially homogeneous starting point, based on the > diffusion > >> of morphogens. The paper begins: > >> > >> "In this section a mathematical model of the growing embryo will be > >> described. This model will be a simplification and an idealization, and > >> consequently a falsification. It is to be hoped that the features > retained > >> for discussion are those of greatest importance in the present state of > >> knowledge." > >> > >> The paper remained virtually uncited for its first 20 years following > >> publication, but since then has amassed 8000 citations (Google > Scholar). The > >> subsequent discovery of huge quantities of molecular detail in > biological > >> pattern formation have only reinforced the importance of this relatively > >> simple model, not because it explains every system, but because the > >> overarching concepts it introduced have proved to be so fertile. > >> > >> Cheers, > >> > >> Geoff > >> > >> > >> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: > >> > >> Ignoring the gross differences in circuitry between hippocampus and > >> cerebellum, etc., is not erring on the side of simplicity, it is erring, > >> period. Have you actually looked at a Cajal/Sxentagothai-style drawing > of > >> their circuitry? > >> > >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: > >> > >> Hi Jim, > >> > >> Focusing too much on the details is risky in and of itself. Optimal > >> compression requires a balance, and we can't compute what that balance > is > >> (all models are wrong). One thing we can say for sure is that we should > err > >> on the side of simplicity, and adding detail to theories before simpler > >> explanations have failed is not Ockham's heuristic. That said it's > still in > >> the space of a Big Data fuzzy science approach, where we throw as much > data > >> from as many levels of analysis as we can come up with into a big pot > and > >> then construct a theory. The thing to keep in mind is that when we start > >> pruning this model most of the details are going to disappear, because > >> almost all of them are irrelevant. Indeed, the size of the description > that > >> includes all the details is almost infinite, whereas the length of the > >> description that explains almost all the variance is extremely short, > >> especially in comparison. This is why Ockham's razor is a good > heuristic. It > >> helps prevent us from wasting time on unnecessary details by suggesting > that > >> we only inquire as to the details once our existing simpler theory has > >> failed to work. > >> > >> On 3/14/14 3:40 PM, Michael Arbib wrote: > >> > >> At 11:17 AM 3/14/2014, Juyang Weng wrote: > >> > >> The brain uses a single architecture to do all brain functions we are > aware > >> of! It uses the same architecture to do vision, audition, motor, > reasoning, > >> decision making, motivation (including pain avoidance and pleasure > seeking, > >> novelty seeking, higher emotion, etc.). > >> > >> > >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were > very > >> different from each other. > >> > >> > >> > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From bower at uthscsa.edu Thu Mar 20 15:26:09 2014 From: bower at uthscsa.edu (james bower) Date: Thu, 20 Mar 2014 14:26:09 -0500 Subject: Connectionists: how the brain works? In-Reply-To: <581625BB6C84AB4BBA1C969C69E269EC025257260DE0@MAVMAIL2.uta.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> , <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> <581625BB6C84AB4BBA1C969C69E269EC025257260DE0@MAVMAIL2.uta.edu> Message-ID: <14D8D78C-E544-4FF0-9A28-D8B93E5C846C@uthscsa.edu> Also add Will Rall - who for years was criticized for building passive dendritic models, even though he clearly believed that this was foundational work for considering active properties - and he was right. However, the care with which he considered the actual structure of biological systems was clearly manifest in his very early work with Gordon Shepherd on mitral cells in the olfactory bulb. This collaboration between an experimentalist and a theorist is still a model of mutual respect and admiration for each other and for the subject at hand. It is also the first case I know of, where modeling of whole neurons made a real, and unexpected PRE-diction (that mitral cell dendrites were electrically coupled). Add to the syllabus for the new introductory graduate course in neuroscience a careful study of this collaboration and its results. So, I think it is worth asking what has happened? Of course, my young children (9,7) are of the opinion that all the music in the 60s was great largely because nobody plays the crap anymore. So in looking back at the sophistication and insights of the folks mentioned one has to be aware of this effect. HOWEVER, my strong sense over the last 30 years is that we have lost a great deal of the rigor and especially, perhaps the breadth necessary for real insight. While there are many reasons to suspect such impressions, especially in those with gray hair, I think several unfortunate trends have contributed: 1) funding sources pushing prematurely at all of us to abandon basic research for something that looks applied 2) a continuing trend in our graduate schools to train technicians with narrow expertise rather than provide and allow the kind of breadth necessary for insight 3) the ability to collect massive amounts of data and bang away at it, instead of thinking. (a kind of scientific ADD) 4) the ongoing routing out of senior faculty at academic institutions who can provide this kind of perspective - but instead are increasingly under the gun to either become overpaid administrators or get grant funding like they did when they were junior faculty 5) I hate to say it, but a kind of self centered laziness that i would say is characteristic in particular of ?my generation? 6) the over weighting of training programs, summer schools, funding opportunities driven by the belief that moving neuroscience forward mostly requires an influx of people trained in physics, engineering etc. With them often comes, Okhams razor. 7) finally, I have very serious concerns about ?cognitive neuroscience? (formerly perhaps physiological psychology) coupled to human imaging studies and the effect it is having on simplifying how we all think about brains and behavior (not to mention its effects on new field like ?neuro-economics" and ?neuromarketing"). Mankind has spent many thousands of years using complex patterns as a framework to organize our thinking about human behavior. For thousands of years we had patterns in the night sky (constellations) coupled to the movement of the planets as a framework to explain human behavior (Astrology). In the 1800?s instead of stars, we used bumps on the head (phrenology). Now, we have the most marvelous device of all, something that makes patterns directly on images of the human brain (brain imaging). Somehow now it all comes down to things like ?executive function?, ?motivation circuits?, ?attention' (whatever that is), etc. all with their corresponding red, blue and green patterns. How different is this really in substance from astrology or as others have pointed out as well, phrenology? And what relationship do these images REALLY have to neuronal function (of course, it has no direct relationship that anyone has been able to understand). For all these reasons, I worry deeply about the drift towards the abstract and the simple - and away from the complexity, where for absolutely certain, the answer lies as the ancient ones appeared to know. :-) Jim On Mar 19, 2014, at 10:36 PM, Levine, Daniel S wrote: > Jim et al., > > To pioneers who understood more about complexity than we often give them credit for I'd add Warren McCulloch. McCulloch and Pitts are clearly well known for their theorem about networks of all-or-none neurons. And yet in about 1969 just before his death I met McCulloch as a graduate student who was just beginning to migrate from pure math into neural modeling. I asked him what area of math I should study in order to be an effective modeler, and he advised me to read a book by a Russian named Minorsky on nonlinear oscillations. > > Best, > Dan Levine > > From: Connectionists [connectionists-bounces at mailman.srv.cs.cmu.edu] On Behalf Of james bower [bower at uthscsa.edu] > Sent: Wednesday, March 19, 2014 9:33 PM > To: Geoffrey Goodhill > Cc: connectionists at mailman.srv.cs.cmu.edu > Subject: Re: Connectionists: how the brain works? > > Geoffrey, > > Nice addition to the discussion actually introducing an interesting angle on the question of brain organization (see below) As you note, reaction diffusion mechanisms and modeling have been quite successful in replicating patterns seen in biology - especially interesting I think is the modeling of patterns in slime molds, but also for very general pattern formation in embryology. However, more and more detailed analysis of what is diffusing, what is sensing what is diffusing, and what is reacting to substances once sensed ? all linked to complex patterns of gene regulation and expression have made it clear that actual embryological development is much much more complex, as Turing himself clearly anticipated, as the quote you cite pretty clearly indicates. Clearly a smart guy. But, I don?t actually think that this is an application of Ochham?s razor although it might appear to be after the fact. Just as Hodgkin and Huxley were not applying it either in their model of the action potential. Turing apparently guessed (based on a lot of work at the time on pattern formation with reaction diffusion) that such a mechanism might provide the natural basis for what embryos do. Thus, just like for Hodgkin and Huxley, his model resulted from a bio-physical insight, not an explicit attempt to build a stripped down model for its own sake. I seriously doubt that Turning would have claimed that he, or his models could more effectively do what biology actually does in forming an embrio, or substitute for the actual process. > > However, I think there is another interesting connection here to the discussion on modeling the brain. Almost certainly communication and organizational systems in early living beings were reaction diffusion based. This is still a dominant effect for many ?sensing? in small organisms. Perhaps, therefore, one can look at nervous systems as structures specifically developed to supersede reaction diffusion mechanisms, thus superseding this very ?natural? but complexity limited type of communication and organization. What this means, I believe, is that a simplified or abstracted physical or mathematical model of the brain explicitly violates the evolutionary pressures responsible for its structure. Its where the wires go, what the wires do, and what the receiving neuron does with the information that forms the basis for neural computation, multiplied by a very large number. And that is dependent on the actual physical structure of those elements. > > One more point about smart guys, as a young computational neurobiologist I questioned how insightful John von Neumann actually was because I was constantly hearing about a lecture he wrote (but didn?t give) at Yale suggesting that dendrites and neurons might be digital ( John von Neumann?s The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) Very clearly a not very insightful idea for a supposedly smart guy. It wasn?t until a few years later, when I actually read the lecture - that I found out that he ends by stating that this idea is almost certainly wrong, given the likely nonlinearities in neuronal dendrites. So von Neumann didn?t lack insight, the people who quoted him did. It is a remarkable fact that more than 60 years later, the majority of models of so called neurons built by engineers AND neurobiologists don?t consider these nonlinearities. The point being the same point, to the Hopfield, Mead, Feynman list, we can now add Turing and von Neumann as suspecting that for understanding, biology and the nervous system must be dealt with in their full complexity. > > But thanks for the example from Turing - always nice to consider actual examples. :-) > > Jim > > > > > > On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill wrote: > >> Hi All, >> >> A great example of successful Ockham-inspired biology is Alan Turing's model for pattern formation (spots, stripes etc) in embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical mechanism for how inhomogeneous spatial patterns can arise in a biological system from a spatially homogeneous starting point, based on the diffusion of morphogens. The paper begins: >> >> "In this section a mathematical model of the growing embryo will be described. This model will be a simplification and an idealization, and consequently a falsification. It is to be hoped that the features retained for discussion are those of greatest importance in the present state of knowledge." >> >> The paper remained virtually uncited for its first 20 years following publication, but since then has amassed 8000 citations (Google Scholar). The subsequent discovery of huge quantities of molecular detail in biological pattern formation have only reinforced the importance of this relatively simple model, not because it explains every system, but because the overarching concepts it introduced have proved to be so fertile. >> >> Cheers, >> >> Geoff >> >> >> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: >> >>> Ignoring the gross differences in circuitry between hippocampus and cerebellum, etc., is not erring on the side of simplicity, it is erring, period. Have you actually looked at a Cajal/Sxentagothai-style drawing of their circuitry? >>> >>> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >>>> Hi Jim, >>>> >>>> Focusing too much on the details is risky in and of itself. Optimal compression requires a balance, and we can't compute what that balance is (all models are wrong). One thing we can say for sure is that we should err on the side of simplicity, and adding detail to theories before simpler explanations have failed is not Ockham's heuristic. That said it's still in the space of a Big Data fuzzy science approach, where we throw as much data from as many levels of analysis as we can come up with into a big pot and then construct a theory. The thing to keep in mind is that when we start pruning this model most of the details are going to disappear, because almost all of them are irrelevant. Indeed, the size of the description that includes all the details is almost infinite, whereas the length of the description that explains almost all the variance is extremely short, especially in comparison. This is why Ockham's razor is a good heuristic. It helps prevent us from wasting time on unnecessary details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. >>>> >>>>> On 3/14/14 3:40 PM, Michael Arbib wrote: >>>>>> At 11:17 AM 3/14/2014, Juyang Weng wrote: >>>>>>> The brain uses a single architecture to do all brain functions we are aware of! It uses the same architecture to do vision, audition, motor, reasoning, decision making, motivation (including pain avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). >>>>>> >>>>>> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very different from each other. -------------- next part -------------- An HTML attachment was scrubbed... URL: From brian.mingus at colorado.edu Thu Mar 20 15:37:19 2014 From: brian.mingus at colorado.edu (Brian J Mingus) Date: Thu, 20 Mar 2014 13:37:19 -0600 Subject: Connectionists: how the brain works? In-Reply-To: <14D8D78C-E544-4FF0-9A28-D8B93E5C846C@uthscsa.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> <581625BB6C84AB4BBA1C969C69E269EC025257260DE0@MAVMAIL2.uta.edu> <14D8D78C-E544-4FF0-9A28-D8B93E5C846C@uthscsa.edu> Message-ID: Having a bunch of people doing whatever they want in parallel is an extremely inefficient way to solve a problem. The way to solve large problems most effectively is to have a few architects at the top design the research paradigm as a system of interacting modules. The architects maintain conceptual integrity and the modules (research programs) can be implemented in parallel. This is not scientific ADD. This is how we put a man on the moon. Nobody is steering this ship, and the end result is either going to take forever, be a big mess that nobody knows how to make sense of, or an AI that takes over the world. It would be much better to defer the direction of our efforts to a team of our best "ADD thinkers" than it is for us all to pick a random topic, pretend it's the most important thing in the universe and then justify it endlessly in order to make ourselves feel better. And with that, I'm out to continue on my merry rational ADD way:) $.02 Brian On Thu, Mar 20, 2014 at 1:26 PM, james bower wrote: > > 3) the ability to collect massive amounts of data and bang away at it, > instead of thinking. (a kind of scientific ADD) > -------------- next part -------------- An HTML attachment was scrubbed... URL: From bower at uthscsa.edu Thu Mar 20 15:42:16 2014 From: bower at uthscsa.edu (james bower) Date: Thu, 20 Mar 2014 14:42:16 -0500 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> <82a4a4bc8bbe49a9906d6dbac9ba314d@DB4PR06MB173.eurprd06.prod.outlook.com> <7127C02E-FECA-4A67-A0E8-E2E5FE7D4926@uthscsa.edu> Message-ID: <06FC2A13-B9C7-4D1B-A220-9A54B71B469B@uthscsa.edu> actually it isn?t - however, it turns out for biology and neurobiology in particular the question of ?brittality? (no auto spell checker not brutality) is a serious one. I may have mentioned previously (my apologies) , but years ago a the Marine Biology Laboratory, I heard a neurobiologist give a presentation at the end of spending an entire summer characterizing all the ion channels in a particular cell in the sea slug (aplasia hermissenda). In his talk, he described those conductances that could explain the cells electrical behavior, and dismissed the majority of the ?small conductances? as likely just sloppiness in gene regulation. From the audience, I asked him if he had changed the temperature of the water bath (Aplasia live in tide pools). The next summer his talk was about the remarkable ability of the neuron to regulate its electrical behavior given changes in the ambient temperature in the tide pool. And guess what, those small conductances were responsible (there is a reason that voltage dependent conductances are highly temperature sensitive). So go ahead and model the sea slug as a round cylinder - unless you deal with the biological details you won?t see the engineering (or understand the function) I claim. Jim On Mar 20, 2014, at 2:00 PM, Tsvi Achler wrote: > Basically it is a definition of over-fitting. The fact large that models may be needed to capture neuron components such proteins and so on do not change the fact that too many extraneous parameters can make the model precise but brittle. > -Tsvi > > > On Thu, Mar 20, 2014 at 10:59 AM, james bower wrote: > Interesting definition - > > just to note, we build realistic biological models with hundreds to thousands of parameters to model a neuron - however, they are not ?free?. > > in fact, it is easier to get a fake model of a neuron to behave like a neuron than it is to make one designed to replicate the anatomy and physiology. Something that many in the physics / engineering worlds don?t realize. So, in fact, by the definition you use, abstract neuronal models which have a smaller number of essentially completely free parameters can?t use Ockham?s shaving cream as cover. :-) > > Again, to return to my old analogy - Kepler was forced BY THE DATA to use ellipses for the orbits of the planets. It is pretty clear he would rather have not. Newton on the other hand clearly benefited from the fact that the moons orbit is essentially circular in his first calculation of the inverse square law. In both cases, however, unlike Ptolomy, they were constructing physically realistic models. Doing so in Biology also requires you deal with complexity you would rather not (as I keep saying ad naus?? ) > > Jim > > > On Mar 20, 2014, at 9:41 AM, Tsvi Achler wrote: > > > I think an Ockham's razor principle can be used to find the most > > optimal algorithm if it is interpreted to mean the model with the > > least amount of free parameters that captures the most phenomena. > > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.pdf > > -Tsvi > > > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz wrote: > >> Ockham works here via compressing both the algorithm and the structure. > >> Compressing the structure to stem cells means that the algorithm should > >> describe the development, the working, and the time dependent structure of > >> the brain. Not compressing the description of the structure of the evolved > >> brain is a different problem since it saves the need for the description of > >> the development, but the working. Understanding the structure and the > >> working of one part of the brain requires the description of its > >> communication that increases the complexity of the description. By the way, > >> this holds for the whole brain, so we might have to include the body at > >> least; a structural minimist may wish to start from the genetic code, use > >> that hint and unfold the already compressed description. There are (many and > >> different) todos 'outside' ... > >> > >> > >> Andras > >> > >> > >> > >> > >> . > >> > >> ________________________________ > >> From: Connectionists on > >> behalf of james bower > >> Sent: Thursday, March 20, 2014 3:33 AM > >> > >> To: Geoffrey Goodhill > >> Cc: connectionists at mailman.srv.cs.cmu.edu > >> Subject: Re: Connectionists: how the brain works? > >> > >> Geoffrey, > >> > >> Nice addition to the discussion actually introducing an interesting angle on > >> the question of brain organization (see below) As you note, reaction > >> diffusion mechanisms and modeling have been quite successful in replicating > >> patterns seen in biology - especially interesting I think is the modeling of > >> patterns in slime molds, but also for very general pattern formation in > >> embryology. However, more and more detailed analysis of what is diffusing, > >> what is sensing what is diffusing, and what is reacting to substances once > >> sensed -- all linked to complex patterns of gene regulation and expression > >> have made it clear that actual embryological development is much much more > >> complex, as Turing himself clearly anticipated, as the quote you cite pretty > >> clearly indicates. Clearly a smart guy. But, I don't actually think that > >> this is an application of Ochham's razor although it might appear to be > >> after the fact. Just as Hodgkin and Huxley were not applying it either in > >> their model of the action potential. Turing apparently guessed (based on a > >> lot of work at the time on pattern formation with reaction diffusion) that > >> such a mechanism might provide the natural basis for what embryos do. Thus, > >> just like for Hodgkin and Huxley, his model resulted from a bio-physical > >> insight, not an explicit attempt to build a stripped down model for its own > >> sake. I seriously doubt that Turning would have claimed that he, or his > >> models could more effectively do what biology actually does in forming an > >> embrio, or substitute for the actual process. > >> > >> However, I think there is another interesting connection here to the > >> discussion on modeling the brain. Almost certainly communication and > >> organizational systems in early living beings were reaction diffusion based. > >> This is still a dominant effect for many 'sensing' in small organisms. > >> Perhaps, therefore, one can look at nervous systems as structures > >> specifically developed to supersede reaction diffusion mechanisms, thus > >> superseding this very 'natural' but complexity limited type of communication > >> and organization. What this means, I believe, is that a simplified or > >> abstracted physical or mathematical model of the brain explicitly violates > >> the evolutionary pressures responsible for its structure. Its where the > >> wires go, what the wires do, and what the receiving neuron does with the > >> information that forms the basis for neural computation, multiplied by a > >> very large number. And that is dependent on the actual physical structure > >> of those elements. > >> > >> One more point about smart guys, as a young computational neurobiologist I > >> questioned how insightful John von Neumann actually was because I was > >> constantly hearing about a lecture he wrote (but didn't give) at Yale > >> suggesting that dendrites and neurons might be digital ( John von Neumann's > >> The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) > >> Very clearly a not very insightful idea for a supposedly smart guy. It > >> wasn't until a few years later, when I actually read the lecture - that I > >> found out that he ends by stating that this idea is almost certainly wrong, > >> given the likely nonlinearities in neuronal dendrites. So von Neumann > >> didn't lack insight, the people who quoted him did. It is a remarkable fact > >> that more than 60 years later, the majority of models of so called neurons > >> built by engineers AND neurobiologists don't consider these nonlinearities. > >> The point being the same point, to the Hopfield, Mead, Feynman list, we can > >> now add Turing and von Neumann as suspecting that for understanding, > >> biology and the nervous system must be dealt with in their full complexity. > >> > >> But thanks for the example from Turing - always nice to consider actual > >> examples. :-) > >> > >> Jim > >> > >> > >> > >> > >> > >> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill wrote: > >> > >> Hi All, > >> > >> A great example of successful Ockham-inspired biology is Alan Turing's model > >> for pattern formation (spots, stripes etc) in embryology (The chemical basis > >> of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical > >> mechanism for how inhomogeneous spatial patterns can arise in a biological > >> system from a spatially homogeneous starting point, based on the diffusion > >> of morphogens. The paper begins: > >> > >> "In this section a mathematical model of the growing embryo will be > >> described. This model will be a simplification and an idealization, and > >> consequently a falsification. It is to be hoped that the features retained > >> for discussion are those of greatest importance in the present state of > >> knowledge." > >> > >> The paper remained virtually uncited for its first 20 years following > >> publication, but since then has amassed 8000 citations (Google Scholar). The > >> subsequent discovery of huge quantities of molecular detail in biological > >> pattern formation have only reinforced the importance of this relatively > >> simple model, not because it explains every system, but because the > >> overarching concepts it introduced have proved to be so fertile. > >> > >> Cheers, > >> > >> Geoff > >> > >> > >> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: > >> > >> Ignoring the gross differences in circuitry between hippocampus and > >> cerebellum, etc., is not erring on the side of simplicity, it is erring, > >> period. Have you actually looked at a Cajal/Sxentagothai-style drawing of > >> their circuitry? > >> > >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: > >> > >> Hi Jim, > >> > >> Focusing too much on the details is risky in and of itself. Optimal > >> compression requires a balance, and we can't compute what that balance is > >> (all models are wrong). One thing we can say for sure is that we should err > >> on the side of simplicity, and adding detail to theories before simpler > >> explanations have failed is not Ockham's heuristic. That said it's still in > >> the space of a Big Data fuzzy science approach, where we throw as much data > >> from as many levels of analysis as we can come up with into a big pot and > >> then construct a theory. The thing to keep in mind is that when we start > >> pruning this model most of the details are going to disappear, because > >> almost all of them are irrelevant. Indeed, the size of the description that > >> includes all the details is almost infinite, whereas the length of the > >> description that explains almost all the variance is extremely short, > >> especially in comparison. This is why Ockham's razor is a good heuristic. It > >> helps prevent us from wasting time on unnecessary details by suggesting that > >> we only inquire as to the details once our existing simpler theory has > >> failed to work. > >> > >> On 3/14/14 3:40 PM, Michael Arbib wrote: > >> > >> At 11:17 AM 3/14/2014, Juyang Weng wrote: > >> > >> The brain uses a single architecture to do all brain functions we are aware > >> of! It uses the same architecture to do vision, audition, motor, reasoning, > >> decision making, motivation (including pain avoidance and pleasure seeking, > >> novelty seeking, higher emotion, etc.). > >> > >> > >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very > >> different from each other. > >> > >> > >> > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From bower at uthscsa.edu Thu Mar 20 15:53:25 2014 From: bower at uthscsa.edu (james bower) Date: Thu, 20 Mar 2014 14:53:25 -0500 Subject: Connectionists: how the brain works? In-Reply-To: References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> <581625BB6C84AB4BBA1C969C69E269EC025257260DE0@MAVMAIL2.uta.edu> <14D8D78C-E544-4FF0-9A28-D8B93E5C846C@uthscsa.edu> Message-ID: <5740BC97-2203-4D2C-96C1-B2C1AD644E6C@uthscsa.edu> On Mar 20, 2014, at 2:37 PM, Brian J Mingus wrote: > Having a bunch of people doing whatever they want in parallel is an extremely inefficient way to solve a problem. on that we agree > The way to solve large problems most effectively is to have a few architects at the top design the research paradigm as a system of interacting modules. On that I also agree - the problem is when using the wrong tools and paradigm. > The architects maintain conceptual integrity and the modules (research programs) can be implemented in parallel. This is not scientific ADD. big data is - IMHO - > This is how we put a man on the moon. Really, we collected all possible trajectories between Florida and the Sea of Tranquility and then once we had collected them into a huge data base, we set about looking for the best trajectory we could find - under the condition that it didn?t require too many parameters to describe? Really? I don?t think so. > Nobody is steering this ship, and the end result is either going to take forever, be a big mess that nobody knows how to make sense of, or an AI that takes over the world. It would be much better to defer the direction of our efforts to a team of our best "ADD thinkers" than it is for us all to pick a random topic, pretend it's the most important thing in the universe and then justify it endlessly in order to make ourselves feel better. We agree completely, in fact I have written about this for years. most recently: https://www.dropbox.com/s/erw705h4yyh3l9k/272602_1_En_5%20copy.pdf (have posted this before) Jim > > And with that, I'm out to continue on my merry rational ADD way:) > > $.02 > > Brian > > On Thu, Mar 20, 2014 at 1:26 PM, james bower wrote: > 3) the ability to collect massive amounts of data and bang away at it, instead of thinking. (a kind of scientific ADD) -------------- next part -------------- An HTML attachment was scrubbed... URL: From troy.d.kelley6.civ at mail.mil Thu Mar 20 16:41:03 2014 From: troy.d.kelley6.civ at mail.mil (Kelley, Troy D CIV (US)) Date: Thu, 20 Mar 2014 20:41:03 +0000 Subject: Connectionists: how the brain works? In-Reply-To: Message-ID: We have found that the habituation algorithm that Sokolov discovered way back in 1963 provides an useful place to start if one is trying to determine how the brain works. The algorithm, at the cellular level, is capable of determining novelty and generating implicit predictions - which it then habituates to. Additionally, it is capable of regenerating the original response when re-exposed to the same stimuli. All of these behaviors provide an excellent framework at the cellular level for explain all sorts of high level behaviors at the functional level. And it fits the Ockham's razor principle of using a single algorithm to explain a wide variety of explicit behavior. Troy D. Kelley RDRL-HRS-E Cognitive Robotics and Modeling Team Leader Human Research and Engineering Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone 410-278-5869 or 410-278-6748 Note my new email address: troy.d.kelley6.civ at mail.mil On 3/20/14 10:41 AM, "Tsvi Achler" wrote: > I think an Ockham's razor principle can be used to find the most > optimal algorithm if it is interpreted to mean the model with the > least amount of free parameters that captures the most phenomena. > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.pdf > -Tsvi > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz wrote: >> Ockham works here via compressing both the algorithm and the structure. >> Compressing the structure to stem cells means that the algorithm should >> describe the development, the working, and the time dependent structure of >> the brain. Not compressing the description of the structure of the evolved >> brain is a different problem since it saves the need for the description of >> the development, but the working. Understanding the structure and the >> working of one part of the brain requires the description of its >> communication that increases the complexity of the description. By the way, >> this holds for the whole brain, so we might have to include the body at >> least; a structural minimist may wish to start from the genetic code, use >> that hint and unfold the already compressed description. There are (many and >> different) todos 'outside' ... >> >> >> Andras >> >> >> >> >> . >> >> ________________________________ >> From: Connectionists on >> behalf of james bower >> Sent: Thursday, March 20, 2014 3:33 AM >> >> To: Geoffrey Goodhill >> Cc: connectionists at mailman.srv.cs.cmu.edu >> Subject: Re: Connectionists: how the brain works? >> >> Geoffrey, >> >> Nice addition to the discussion actually introducing an interesting angle on >> the question of brain organization (see below) As you note, reaction >> diffusion mechanisms and modeling have been quite successful in replicating >> patterns seen in biology - especially interesting I think is the modeling of >> patterns in slime molds, but also for very general pattern formation in >> embryology. However, more and more detailed analysis of what is diffusing, >> what is sensing what is diffusing, and what is reacting to substances once >> sensed -- all linked to complex patterns of gene regulation and expression >> have made it clear that actual embryological development is much much more >> complex, as Turing himself clearly anticipated, as the quote you cite pretty >> clearly indicates. Clearly a smart guy. But, I don't actually think that >> this is an application of Ochham's razor although it might appear to be >> after the fact. Just as Hodgkin and Huxley were not applying it either in >> their model of the action potential. Turing apparently guessed (based on a >> lot of work at the time on pattern formation with reaction diffusion) that >> such a mechanism might provide the natural basis for what embryos do. Thus, >> just like for Hodgkin and Huxley, his model resulted from a bio-physical >> insight, not an explicit attempt to build a stripped down model for its own >> sake. I seriously doubt that Turning would have claimed that he, or his >> models could more effectively do what biology actually does in forming an >> embrio, or substitute for the actual process. >> >> However, I think there is another interesting connection here to the >> discussion on modeling the brain. Almost certainly communication and >> organizational systems in early living beings were reaction diffusion based. >> This is still a dominant effect for many 'sensing' in small organisms. >> Perhaps, therefore, one can look at nervous systems as structures >> specifically developed to supersede reaction diffusion mechanisms, thus >> superseding this very 'natural' but complexity limited type of communication >> and organization. What this means, I believe, is that a simplified or >> abstracted physical or mathematical model of the brain explicitly violates >> the evolutionary pressures responsible for its structure. Its where the >> wires go, what the wires do, and what the receiving neuron does with the >> information that forms the basis for neural computation, multiplied by a >> very large number. And that is dependent on the actual physical structure >> of those elements. >> >> One more point about smart guys, as a young computational neurobiologist I >> questioned how insightful John von Neumann actually was because I was >> constantly hearing about a lecture he wrote (but didn't give) at Yale >> suggesting that dendrites and neurons might be digital ( John von Neumann's >> The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) >> Very clearly a not very insightful idea for a supposedly smart guy. It >> wasn't until a few years later, when I actually read the lecture - that I >> found out that he ends by stating that this idea is almost certainly wrong, >> given the likely nonlinearities in neuronal dendrites. So von Neumann >> didn't lack insight, the people who quoted him did. It is a remarkable fact >> that more than 60 years later, the majority of models of so called neurons >> built by engineers AND neurobiologists don't consider these nonlinearities. >> The point being the same point, to the Hopfield, Mead, Feynman list, we can >> now add Turing and von Neumann as suspecting that for understanding, >> biology and the nervous system must be dealt with in their full complexity. >> >> But thanks for the example from Turing - always nice to consider actual >> examples. :-) >> >> Jim >> >> >> >> >> >> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill wrote: >> >> Hi All, >> >> A great example of successful Ockham-inspired biology is Alan Turing's model >> for pattern formation (spots, stripes etc) in embryology (The chemical basis >> of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical >> mechanism for how inhomogeneous spatial patterns can arise in a biological >> system from a spatially homogeneous starting point, based on the diffusion >> of morphogens. The paper begins: >> >> "In this section a mathematical model of the growing embryo will be >> described. This model will be a simplification and an idealization, and >> consequently a falsification. It is to be hoped that the features retained >> for discussion are those of greatest importance in the present state of >> knowledge." >> >> The paper remained virtually uncited for its first 20 years following >> publication, but since then has amassed 8000 citations (Google Scholar). The >> subsequent discovery of huge quantities of molecular detail in biological >> pattern formation have only reinforced the importance of this relatively >> simple model, not because it explains every system, but because the >> overarching concepts it introduced have proved to be so fertile. >> >> Cheers, >> >> Geoff >> >> >> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: >> >> Ignoring the gross differences in circuitry between hippocampus and >> cerebellum, etc., is not erring on the side of simplicity, it is erring, >> period. Have you actually looked at a Cajal/Sxentagothai-style drawing of >> their circuitry? >> >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >> >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. Optimal >> compression requires a balance, and we can't compute what that balance is >> (all models are wrong). One thing we can say for sure is that we should err >> on the side of simplicity, and adding detail to theories before simpler >> explanations have failed is not Ockham's heuristic. That said it's still in >> the space of a Big Data fuzzy science approach, where we throw as much data >> from as many levels of analysis as we can come up with into a big pot and >> then construct a theory. The thing to keep in mind is that when we start >> pruning this model most of the details are going to disappear, because >> almost all of them are irrelevant. Indeed, the size of the description that >> includes all the details is almost infinite, whereas the length of the >> description that explains almost all the variance is extremely short, >> especially in comparison. This is why Ockham's razor is a good heuristic. It >> helps prevent us from wasting time on unnecessary details by suggesting that >> we only inquire as to the details once our existing simpler theory has >> failed to work. >> >> On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >> The brain uses a single architecture to do all brain functions we are aware >> of! It uses the same architecture to do vision, audition, motor, reasoning, >> decision making, motivation (including pain avoidance and pleasure seeking, >> novelty seeking, higher emotion, etc.). >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very >> different from each other. >> >> >> Troy D. Kelley RDRL-HRS-E Cognitive Robotics and Modeling Team Leader Human Research and Engineering Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone 410-278-5869 or 410-278-6748 Note my new email address: troy.d.kelley6.civ at mail.mil From mhb0 at lehigh.edu Thu Mar 20 17:28:20 2014 From: mhb0 at lehigh.edu (Mark H. Bickhard) Date: Thu, 20 Mar 2014 17:28:20 -0400 Subject: Connectionists: how the brain works? In-Reply-To: References: Message-ID: <51FDF3A2-717D-4399-9076-331EEA0FC45A@lehigh.edu> I would agree with the importance of Sokolov habituation, but there is more than one way to understand and generalize from this phenomenon: http://www.lehigh.edu/~mhb0/AnticipatoryBrain20Aug13.pdf Mark H. Bickhard Lehigh University 17 Memorial Drive East Bethlehem, PA 18015 mark at bickhard.name http://bickhard.ws/ On Mar 20, 2014, at 4:41 PM, Kelley, Troy D CIV (US) wrote: We have found that the habituation algorithm that Sokolov discovered way back in 1963 provides an useful place to start if one is trying to determine how the brain works. The algorithm, at the cellular level, is capable of determining novelty and generating implicit predictions - which it then habituates to. Additionally, it is capable of regenerating the original response when re-exposed to the same stimuli. All of these behaviors provide an excellent framework at the cellular level for explain all sorts of high level behaviors at the functional level. And it fits the Ockham's razor principle of using a single algorithm to explain a wide variety of explicit behavior. Troy D. Kelley RDRL-HRS-E Cognitive Robotics and Modeling Team Leader Human Research and Engineering Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone 410-278-5869 or 410-278-6748 Note my new email address: troy.d.kelley6.civ at mail.mil On 3/20/14 10:41 AM, "Tsvi Achler" wrote: > I think an Ockham's razor principle can be used to find the most > optimal algorithm if it is interpreted to mean the model with the > least amount of free parameters that captures the most phenomena. > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.pdf > -Tsvi > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz wrote: >> Ockham works here via compressing both the algorithm and the structure. >> Compressing the structure to stem cells means that the algorithm should >> describe the development, the working, and the time dependent structure of >> the brain. Not compressing the description of the structure of the evolved >> brain is a different problem since it saves the need for the description of >> the development, but the working. Understanding the structure and the >> working of one part of the brain requires the description of its >> communication that increases the complexity of the description. By the way, >> this holds for the whole brain, so we might have to include the body at >> least; a structural minimist may wish to start from the genetic code, use >> that hint and unfold the already compressed description. There are (many and >> different) todos 'outside' ... >> >> >> Andras >> >> >> >> >> . >> >> ________________________________ >> From: Connectionists on >> behalf of james bower >> Sent: Thursday, March 20, 2014 3:33 AM >> >> To: Geoffrey Goodhill >> Cc: connectionists at mailman.srv.cs.cmu.edu >> Subject: Re: Connectionists: how the brain works? >> >> Geoffrey, >> >> Nice addition to the discussion actually introducing an interesting angle on >> the question of brain organization (see below) As you note, reaction >> diffusion mechanisms and modeling have been quite successful in replicating >> patterns seen in biology - especially interesting I think is the modeling of >> patterns in slime molds, but also for very general pattern formation in >> embryology. However, more and more detailed analysis of what is diffusing, >> what is sensing what is diffusing, and what is reacting to substances once >> sensed -- all linked to complex patterns of gene regulation and expression >> have made it clear that actual embryological development is much much more >> complex, as Turing himself clearly anticipated, as the quote you cite pretty >> clearly indicates. Clearly a smart guy. But, I don't actually think that >> this is an application of Ochham's razor although it might appear to be >> after the fact. Just as Hodgkin and Huxley were not applying it either in >> their model of the action potential. Turing apparently guessed (based on a >> lot of work at the time on pattern formation with reaction diffusion) that >> such a mechanism might provide the natural basis for what embryos do. Thus, >> just like for Hodgkin and Huxley, his model resulted from a bio-physical >> insight, not an explicit attempt to build a stripped down model for its own >> sake. I seriously doubt that Turning would have claimed that he, or his >> models could more effectively do what biology actually does in forming an >> embrio, or substitute for the actual process. >> >> However, I think there is another interesting connection here to the >> discussion on modeling the brain. Almost certainly communication and >> organizational systems in early living beings were reaction diffusion based. >> This is still a dominant effect for many 'sensing' in small organisms. >> Perhaps, therefore, one can look at nervous systems as structures >> specifically developed to supersede reaction diffusion mechanisms, thus >> superseding this very 'natural' but complexity limited type of communication >> and organization. What this means, I believe, is that a simplified or >> abstracted physical or mathematical model of the brain explicitly violates >> the evolutionary pressures responsible for its structure. Its where the >> wires go, what the wires do, and what the receiving neuron does with the >> information that forms the basis for neural computation, multiplied by a >> very large number. And that is dependent on the actual physical structure >> of those elements. >> >> One more point about smart guys, as a young computational neurobiologist I >> questioned how insightful John von Neumann actually was because I was >> constantly hearing about a lecture he wrote (but didn't give) at Yale >> suggesting that dendrites and neurons might be digital ( John von Neumann's >> The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) >> Very clearly a not very insightful idea for a supposedly smart guy. It >> wasn't until a few years later, when I actually read the lecture - that I >> found out that he ends by stating that this idea is almost certainly wrong, >> given the likely nonlinearities in neuronal dendrites. So von Neumann >> didn't lack insight, the people who quoted him did. It is a remarkable fact >> that more than 60 years later, the majority of models of so called neurons >> built by engineers AND neurobiologists don't consider these nonlinearities. >> The point being the same point, to the Hopfield, Mead, Feynman list, we can >> now add Turing and von Neumann as suspecting that for understanding, >> biology and the nervous system must be dealt with in their full complexity. >> >> But thanks for the example from Turing - always nice to consider actual >> examples. :-) >> >> Jim >> >> >> >> >> >> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill wrote: >> >> Hi All, >> >> A great example of successful Ockham-inspired biology is Alan Turing's model >> for pattern formation (spots, stripes etc) in embryology (The chemical basis >> of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical >> mechanism for how inhomogeneous spatial patterns can arise in a biological >> system from a spatially homogeneous starting point, based on the diffusion >> of morphogens. The paper begins: >> >> "In this section a mathematical model of the growing embryo will be >> described. This model will be a simplification and an idealization, and >> consequently a falsification. It is to be hoped that the features retained >> for discussion are those of greatest importance in the present state of >> knowledge." >> >> The paper remained virtually uncited for its first 20 years following >> publication, but since then has amassed 8000 citations (Google Scholar). The >> subsequent discovery of huge quantities of molecular detail in biological >> pattern formation have only reinforced the importance of this relatively >> simple model, not because it explains every system, but because the >> overarching concepts it introduced have proved to be so fertile. >> >> Cheers, >> >> Geoff >> >> >> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: >> >> Ignoring the gross differences in circuitry between hippocampus and >> cerebellum, etc., is not erring on the side of simplicity, it is erring, >> period. Have you actually looked at a Cajal/Sxentagothai-style drawing of >> their circuitry? >> >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >> >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. Optimal >> compression requires a balance, and we can't compute what that balance is >> (all models are wrong). One thing we can say for sure is that we should err >> on the side of simplicity, and adding detail to theories before simpler >> explanations have failed is not Ockham's heuristic. That said it's still in >> the space of a Big Data fuzzy science approach, where we throw as much data >> from as many levels of analysis as we can come up with into a big pot and >> then construct a theory. The thing to keep in mind is that when we start >> pruning this model most of the details are going to disappear, because >> almost all of them are irrelevant. Indeed, the size of the description that >> includes all the details is almost infinite, whereas the length of the >> description that explains almost all the variance is extremely short, >> especially in comparison. This is why Ockham's razor is a good heuristic. It >> helps prevent us from wasting time on unnecessary details by suggesting that >> we only inquire as to the details once our existing simpler theory has >> failed to work. >> >> On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >> The brain uses a single architecture to do all brain functions we are aware >> of! It uses the same architecture to do vision, audition, motor, reasoning, >> decision making, motivation (including pain avoidance and pleasure seeking, >> novelty seeking, higher emotion, etc.). >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very >> different from each other. >> >> >> Troy D. Kelley RDRL-HRS-E Cognitive Robotics and Modeling Team Leader Human Research and Engineering Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone 410-278-5869 or 410-278-6748 Note my new email address: troy.d.kelley6.civ at mail.mil From lnl at psu.edu Thu Mar 20 17:28:15 2014 From: lnl at psu.edu (Lyle N. Long) Date: Thu, 20 Mar 2014 17:28:15 -0400 Subject: Connectionists: how the brain works? In-Reply-To: References: Message-ID: <1FD6D189-77AE-41D0-8B87-53E6AC175CE9@psu.edu> Troy, is the the paper you are referring to? "Higher Nervous Functions: The Orienting Reflex" Annual Review of Physiology Vol. 25: 545-580 (Volume publication date March 1963) DOI: 10.1146/annurev.ph.25.030163.002553 E N Sokolov Just curious. Lyle ----------------------------------------------- Prof. Lyle N. Long The Pennsylvania State University http://www.personal.psu.edu/lnl lnl at psu.edu On Mar 20, 2014, at 4:41 PM, Kelley, Troy D CIV (US) wrote: > We have found that the habituation algorithm that Sokolov discovered way > back in 1963 provides an useful place to start if one is trying to determine > how the brain works. The algorithm, at the cellular level, is capable of > determining novelty and generating implicit predictions - which it then > habituates to. Additionally, it is capable of regenerating the original > response when re-exposed to the same stimuli. All of these behaviors > provide an excellent framework at the cellular level for explain all sorts > of high level behaviors at the functional level. And it fits the Ockham's > razor principle of using a single algorithm to explain a wide variety of > explicit behavior. > > Troy D. Kelley > RDRL-HRS-E > Cognitive Robotics and Modeling Team Leader > Human Research and Engineering Directorate > U.S. Army Research Laboratory > Aberdeen, MD 21005 > Phone 410-278-5869 or 410-278-6748 > Note my new email address: troy.d.kelley6.civ at mail.mil > > > > > > On 3/20/14 10:41 AM, "Tsvi Achler" wrote: > >> I think an Ockham's razor principle can be used to find the most >> optimal algorithm if it is interpreted to mean the model with the >> least amount of free parameters that captures the most phenomena. >> http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.pdf >> -Tsvi >> >> On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz wrote: >>> Ockham works here via compressing both the algorithm and the structure. >>> Compressing the structure to stem cells means that the algorithm should >>> describe the development, the working, and the time dependent structure of >>> the brain. Not compressing the description of the structure of the evolved >>> brain is a different problem since it saves the need for the description of >>> the development, but the working. Understanding the structure and the >>> working of one part of the brain requires the description of its >>> communication that increases the complexity of the description. By the way, >>> this holds for the whole brain, so we might have to include the body at >>> least; a structural minimist may wish to start from the genetic code, use >>> that hint and unfold the already compressed description. There are (many and >>> different) todos 'outside' ... >>> >>> >>> Andras >>> >>> >>> >>> >>> . >>> >>> ________________________________ >>> From: Connectionists on >>> behalf of james bower >>> Sent: Thursday, March 20, 2014 3:33 AM >>> >>> To: Geoffrey Goodhill >>> Cc: connectionists at mailman.srv.cs.cmu.edu >>> Subject: Re: Connectionists: how the brain works? >>> >>> Geoffrey, >>> >>> Nice addition to the discussion actually introducing an interesting angle on >>> the question of brain organization (see below) As you note, reaction >>> diffusion mechanisms and modeling have been quite successful in replicating >>> patterns seen in biology - especially interesting I think is the modeling of >>> patterns in slime molds, but also for very general pattern formation in >>> embryology. However, more and more detailed analysis of what is diffusing, >>> what is sensing what is diffusing, and what is reacting to substances once >>> sensed -- all linked to complex patterns of gene regulation and expression >>> have made it clear that actual embryological development is much much more >>> complex, as Turing himself clearly anticipated, as the quote you cite pretty >>> clearly indicates. Clearly a smart guy. But, I don't actually think that >>> this is an application of Ochham's razor although it might appear to be >>> after the fact. Just as Hodgkin and Huxley were not applying it either in >>> their model of the action potential. Turing apparently guessed (based on a >>> lot of work at the time on pattern formation with reaction diffusion) that >>> such a mechanism might provide the natural basis for what embryos do. Thus, >>> just like for Hodgkin and Huxley, his model resulted from a bio-physical >>> insight, not an explicit attempt to build a stripped down model for its own >>> sake. I seriously doubt that Turning would have claimed that he, or his >>> models could more effectively do what biology actually does in forming an >>> embrio, or substitute for the actual process. >>> >>> However, I think there is another interesting connection here to the >>> discussion on modeling the brain. Almost certainly communication and >>> organizational systems in early living beings were reaction diffusion based. >>> This is still a dominant effect for many 'sensing' in small organisms. >>> Perhaps, therefore, one can look at nervous systems as structures >>> specifically developed to supersede reaction diffusion mechanisms, thus >>> superseding this very 'natural' but complexity limited type of communication >>> and organization. What this means, I believe, is that a simplified or >>> abstracted physical or mathematical model of the brain explicitly violates >>> the evolutionary pressures responsible for its structure. Its where the >>> wires go, what the wires do, and what the receiving neuron does with the >>> information that forms the basis for neural computation, multiplied by a >>> very large number. And that is dependent on the actual physical structure >>> of those elements. >>> >>> One more point about smart guys, as a young computational neurobiologist I >>> questioned how insightful John von Neumann actually was because I was >>> constantly hearing about a lecture he wrote (but didn't give) at Yale >>> suggesting that dendrites and neurons might be digital ( John von Neumann's >>> The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) >>> Very clearly a not very insightful idea for a supposedly smart guy. It >>> wasn't until a few years later, when I actually read the lecture - that I >>> found out that he ends by stating that this idea is almost certainly wrong, >>> given the likely nonlinearities in neuronal dendrites. So von Neumann >>> didn't lack insight, the people who quoted him did. It is a remarkable fact >>> that more than 60 years later, the majority of models of so called neurons >>> built by engineers AND neurobiologists don't consider these nonlinearities. >>> The point being the same point, to the Hopfield, Mead, Feynman list, we can >>> now add Turing and von Neumann as suspecting that for understanding, >>> biology and the nervous system must be dealt with in their full complexity. >>> >>> But thanks for the example from Turing - always nice to consider actual >>> examples. :-) >>> >>> Jim >>> >>> >>> >>> >>> >>> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill wrote: >>> >>> Hi All, >>> >>> A great example of successful Ockham-inspired biology is Alan Turing's model >>> for pattern formation (spots, stripes etc) in embryology (The chemical basis >>> of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical >>> mechanism for how inhomogeneous spatial patterns can arise in a biological >>> system from a spatially homogeneous starting point, based on the diffusion >>> of morphogens. The paper begins: >>> >>> "In this section a mathematical model of the growing embryo will be >>> described. This model will be a simplification and an idealization, and >>> consequently a falsification. It is to be hoped that the features retained >>> for discussion are those of greatest importance in the present state of >>> knowledge." >>> >>> The paper remained virtually uncited for its first 20 years following >>> publication, but since then has amassed 8000 citations (Google Scholar). The >>> subsequent discovery of huge quantities of molecular detail in biological >>> pattern formation have only reinforced the importance of this relatively >>> simple model, not because it explains every system, but because the >>> overarching concepts it introduced have proved to be so fertile. >>> >>> Cheers, >>> >>> Geoff >>> >>> >>> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: >>> >>> Ignoring the gross differences in circuitry between hippocampus and >>> cerebellum, etc., is not erring on the side of simplicity, it is erring, >>> period. Have you actually looked at a Cajal/Sxentagothai-style drawing of >>> their circuitry? >>> >>> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >>> >>> Hi Jim, >>> >>> Focusing too much on the details is risky in and of itself. Optimal >>> compression requires a balance, and we can't compute what that balance is >>> (all models are wrong). One thing we can say for sure is that we should err >>> on the side of simplicity, and adding detail to theories before simpler >>> explanations have failed is not Ockham's heuristic. That said it's still in >>> the space of a Big Data fuzzy science approach, where we throw as much data >>> from as many levels of analysis as we can come up with into a big pot and >>> then construct a theory. The thing to keep in mind is that when we start >>> pruning this model most of the details are going to disappear, because >>> almost all of them are irrelevant. Indeed, the size of the description that >>> includes all the details is almost infinite, whereas the length of the >>> description that explains almost all the variance is extremely short, >>> especially in comparison. This is why Ockham's razor is a good heuristic. It >>> helps prevent us from wasting time on unnecessary details by suggesting that >>> we only inquire as to the details once our existing simpler theory has >>> failed to work. >>> >>> On 3/14/14 3:40 PM, Michael Arbib wrote: >>> >>> At 11:17 AM 3/14/2014, Juyang Weng wrote: >>> >>> The brain uses a single architecture to do all brain functions we are aware >>> of! It uses the same architecture to do vision, audition, motor, reasoning, >>> decision making, motivation (including pain avoidance and pleasure seeking, >>> novelty seeking, higher emotion, etc.). >>> >>> >>> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very >>> different from each other. >>> >>> >>> > > Troy D. Kelley > RDRL-HRS-E > Cognitive Robotics and Modeling Team Leader > Human Research and Engineering Directorate > U.S. Army Research Laboratory > Aberdeen, MD 21005 > Phone 410-278-5869 or 410-278-6748 > Note my new email address: troy.d.kelley6.civ at mail.mil > > From weng at cse.msu.edu Thu Mar 20 21:17:48 2014 From: weng at cse.msu.edu (Juyang Weng) Date: Thu, 20 Mar 2014 21:17:48 -0400 Subject: Connectionists: how the brain works? In-Reply-To: <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> Message-ID: <532B933C.6060701@cse.msu.edu> Mike, the main idea for Autonomous Mental Development (AMD) is to automatically generate (develop) brain regions and detailed circuits recursively (scaffolding) using three things: (1) The developmental program (simulating the functions of the genome) (2) The circuits already generated so far from the birth time (3) The experience of autonomous interactions with the environments. Why? The brain circuits are too complex to hand craft. Every brain circuit is very different. For this reason, I think that the NIH Connectome project is not very useful for understanding how the brain works even one gets the perfect connection map without any error. We have shown that different areas of the DN so developed indeed have very different circuits. However, we have not yet identified which part of a DN is hippocampus and which part is cerebellum, etc., because we still mainly use one or two sensory modalities (e.g., vision and audition, or vision and touch) and a very small set of effectors. A hippocampus uses all five sensory modalities of a human body and all human body effectors to develop. A cerebellum uses very rich sets of receptors in the somatosensory system and almost all body effectors. To truly generate (develop) hippocampus and cerebellum correctly, one needs to have at least all major receptors and effectors that they use. Therefore, I do not think that anybody should statically model a hippocampus or a cerebellum because without the developmental causality, any such static circuits are grossly wrong and computationally inefficient. In summary, the brain is nearly optimally developed to perform the sensorimotor experience that a child/human has experience up to the current time. This is probably not a view supported by most people on this list, but I respectfully ask everybody to at least consider patiently. -John On 3/19/14 4:27 PM, Michael Arbib wrote: > Ignoring the gross differences in circuitry between hippocampus and > cerebellum, etc., is not erring on the side of simplicity, it is > erring, period. Have you actually looked at a Cajal/Sxentagothai-style > drawing of their circuitry? > > At 01:07 PM 3/19/2014, Brian J Mingus wrote: >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. Optimal >> compression requires a balance, and we can't compute what that >> balance is (all models are wrong). One thing we can say for sure is >> that we should err on the side of simplicity, and adding detail to >> theories before simpler explanations have failed is not Ockham's >> heuristic. That said it's still in the space of a Big Data fuzzy >> science approach, where we throw as much data from as many levels of >> analysis as we can come up with into a big pot and then construct a >> theory. The thing to keep in mind is that when we start pruning this >> model most of the details are going to disappear, because almost all >> of them are irrelevant. Indeed, the size of the description that >> includes all the details is almost infinite, whereas the length of >> the description that explains almost all the variance is extremely >> short, especially in comparison. This is why Ockham's razor is a good >> heuristic. It helps prevent us from wasting time on unnecessary >> details by suggesting that we only inquire as to the details once our >> existing simpler theory has failed to work. >> >> > On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >>> The brain uses a single architecture to do all brain >> functions we are aware of! It uses the same architecture to do >> vision, audition, motor, reasoning, decision making, motivation >> (including pain avoidance and pleasure seeking, novelty seeking, >> higher emotion, etc.). >> >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and >> cerebellum were very different from each other. >> -- -- Juyang (John) Weng, Professor Department of Computer Science and Engineering MSU Cognitive Science Program and MSU Neuroscience Program 428 S Shaw Ln Rm 3115 Michigan State University East Lansing, MI 48824 USA Tel: 517-353-4388 Fax: 517-432-1061 Email: weng at cse.msu.edu URL: http://www.cse.msu.edu/~weng/ ---------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From troy.d.kelley6.civ at mail.mil Fri Mar 21 08:24:29 2014 From: troy.d.kelley6.civ at mail.mil (Kelley, Troy D CIV (US)) Date: Fri, 21 Mar 2014 12:24:29 +0000 Subject: Connectionists: how the brain works? (UNCLASSIFIED) In-Reply-To: <51FDF3A2-717D-4399-9076-331EEA0FC45A@lehigh.edu> References: <51FDF3A2-717D-4399-9076-331EEA0FC45A@lehigh.edu> Message-ID: Classification: UNCLASSIFIED Caveats: NONE Yes, Mark, I would argue that habituation is anticipatory prediction. The neuron creates a model of the incoming stimulus and the neuron is essentially predicting that the next stimuli will be comparatively similar to the previous stimulus. If this prediction is met, the neuron habituates. That is a simple, low level, predictive model. -----Original Message----- From: Mark H. Bickhard [mailto:mhb0 at Lehigh.EDU] Sent: Thursday, March 20, 2014 5:28 PM To: Kelley, Troy D CIV (US) Cc: Tsvi Achler; Andras Lorincz; bower at uthscsa.edu; connectionists at mailman.srv.cs.cmu.edu Subject: Re: Connectionists: how the brain works? I would agree with the importance of Sokolov habituation, but there is more than one way to understand and generalize from this phenomenon: http://www.lehigh.edu/~mhb0/AnticipatoryBrain20Aug13.pdf Mark H. Bickhard Lehigh University 17 Memorial Drive East Bethlehem, PA 18015 mark at bickhard.name http://bickhard.ws/ On Mar 20, 2014, at 4:41 PM, Kelley, Troy D CIV (US) wrote: We have found that the habituation algorithm that Sokolov discovered way back in 1963 provides an useful place to start if one is trying to determine how the brain works. The algorithm, at the cellular level, is capable of determining novelty and generating implicit predictions - which it then habituates to. Additionally, it is capable of regenerating the original response when re-exposed to the same stimuli. All of these behaviors provide an excellent framework at the cellular level for explain all sorts of high level behaviors at the functional level. And it fits the Ockham's razor principle of using a single algorithm to explain a wide variety of explicit behavior. Troy D. Kelley RDRL-HRS-E Cognitive Robotics and Modeling Team Leader Human Research and Engineering Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone 410-278-5869 or 410-278-6748 Note my new email address: troy.d.kelley6.civ at mail.mil On 3/20/14 10:41 AM, "Tsvi Achler" wrote: > I think an Ockham's razor principle can be used to find the most > optimal algorithm if it is interpreted to mean the model with the > least amount of free parameters that captures the most phenomena. > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.p > df > -Tsvi > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz wrote: >> Ockham works here via compressing both the algorithm and the structure. >> Compressing the structure to stem cells means that the algorithm >> should describe the development, the working, and the time dependent >> structure of the brain. Not compressing the description of the >> structure of the evolved brain is a different problem since it saves >> the need for the description of the development, but the working. >> Understanding the structure and the working of one part of the brain >> requires the description of its communication that increases the >> complexity of the description. By the way, this holds for the whole >> brain, so we might have to include the body at least; a structural >> minimist may wish to start from the genetic code, use that hint and >> unfold the already compressed description. There are (many and >> different) todos 'outside' ... >> >> >> Andras >> >> >> >> >> . >> >> ________________________________ >> From: Connectionists >> on behalf of james bower >> Sent: Thursday, March 20, 2014 3:33 AM >> >> To: Geoffrey Goodhill >> Cc: connectionists at mailman.srv.cs.cmu.edu >> Subject: Re: Connectionists: how the brain works? >> >> Geoffrey, >> >> Nice addition to the discussion actually introducing an interesting >> angle on the question of brain organization (see below) As you note, >> reaction diffusion mechanisms and modeling have been quite successful >> in replicating patterns seen in biology - especially interesting I >> think is the modeling of patterns in slime molds, but also for very >> general pattern formation in embryology. However, more and more >> detailed analysis of what is diffusing, what is sensing what is >> diffusing, and what is reacting to substances once sensed -- all >> linked to complex patterns of gene regulation and expression have >> made it clear that actual embryological development is much much more complex, as Turing himself clearly anticipated, as the quote you cite pretty >> clearly indicates. Clearly a smart guy. But, I don't actually think that >> this is an application of Ochham's razor although it might appear to >> be after the fact. Just as Hodgkin and Huxley were not applying it either in >> their model of the action potential. Turing apparently guessed (based on a >> lot of work at the time on pattern formation with reaction diffusion) >> that such a mechanism might provide the natural basis for what >> embryos do. Thus, just like for Hodgkin and Huxley, his model >> resulted from a bio-physical insight, not an explicit attempt to >> build a stripped down model for its own sake. I seriously doubt >> that Turning would have claimed that he, or his models could more >> effectively do what biology actually does in forming an embrio, or substitute for the actual process. >> >> However, I think there is another interesting connection here to the >> discussion on modeling the brain. Almost certainly communication and >> organizational systems in early living beings were reaction diffusion based. >> This is still a dominant effect for many 'sensing' in small organisms. >> Perhaps, therefore, one can look at nervous systems as structures >> specifically developed to supersede reaction diffusion mechanisms, >> thus superseding this very 'natural' but complexity limited type of >> communication and organization. What this means, I believe, is that >> a simplified or abstracted physical or mathematical model of the >> brain explicitly violates the evolutionary pressures responsible for >> its structure. Its where the wires go, what the wires do, and what >> the receiving neuron does with the information that forms the basis >> for neural computation, multiplied by a very large number. And that >> is dependent on the actual physical structure of those elements. >> >> One more point about smart guys, as a young computational >> neurobiologist I questioned how insightful John von Neumann actually >> was because I was constantly hearing about a lecture he wrote (but >> didn't give) at Yale suggesting that dendrites and neurons might be >> digital ( John von Neumann's The Computer and the Brain. (New >> Haven/London: Yale Univesity Press, 1958.) Very clearly a not very >> insightful idea for a supposedly smart guy. It wasn't until a few >> years later, when I actually read the lecture - that I found out that >> he ends by stating that this idea is almost certainly wrong, given >> the likely nonlinearities in neuronal dendrites. So von Neumann >> didn't lack insight, the people who quoted him did. It is a >> remarkable fact that more than 60 years later, the majority of models of so called neurons built by engineers AND neurobiologists don't consider these nonlinearities. >> The point being the same point, to the Hopfield, Mead, Feynman list, >> we can now add Turing and von Neumann as suspecting that for >> understanding, biology and the nervous system must be dealt with in their full complexity. >> >> But thanks for the example from Turing - always nice to consider actual >> examples. :-) >> >> Jim >> >> >> >> >> >> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill wrote: >> >> Hi All, >> >> A great example of successful Ockham-inspired biology is Alan >> Turing's model for pattern formation (spots, stripes etc) in >> embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, >> 1953). Turing introduced a physical mechanism for how inhomogeneous >> spatial patterns can arise in a biological system from a spatially >> homogeneous starting point, based on the diffusion of morphogens. The paper begins: >> >> "In this section a mathematical model of the growing embryo will be >> described. This model will be a simplification and an idealization, >> and consequently a falsification. It is to be hoped that the features >> retained for discussion are those of greatest importance in the >> present state of knowledge." >> >> The paper remained virtually uncited for its first 20 years following >> publication, but since then has amassed 8000 citations (Google >> Scholar). The subsequent discovery of huge quantities of molecular >> detail in biological pattern formation have only reinforced the >> importance of this relatively simple model, not because it explains >> every system, but because the overarching concepts it introduced have proved to be so fertile. >> >> Cheers, >> >> Geoff >> >> >> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: >> >> Ignoring the gross differences in circuitry between hippocampus and >> cerebellum, etc., is not erring on the side of simplicity, it is >> erring, period. Have you actually looked at a >> Cajal/Sxentagothai-style drawing of their circuitry? >> >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >> >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. Optimal >> compression requires a balance, and we can't compute what that >> balance is (all models are wrong). One thing we can say for sure is >> that we should err on the side of simplicity, and adding detail to >> theories before simpler explanations have failed is not Ockham's >> heuristic. That said it's still in the space of a Big Data fuzzy >> science approach, where we throw as much data from as many levels of >> analysis as we can come up with into a big pot and then construct a >> theory. The thing to keep in mind is that when we start pruning this >> model most of the details are going to disappear, because almost all >> of them are irrelevant. Indeed, the size of the description that >> includes all the details is almost infinite, whereas the length of >> the description that explains almost all the variance is extremely >> short, especially in comparison. This is why Ockham's razor is a good >> heuristic. It helps prevent us from wasting time on unnecessary >> details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. >> >> On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >> The brain uses a single architecture to do all brain functions we are >> aware of! It uses the same architecture to do vision, audition, >> motor, reasoning, decision making, motivation (including pain >> avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum >> were very different from each other. >> >> >> Troy D. Kelley RDRL-HRS-E Cognitive Robotics and Modeling Team Leader Human Research and Engineering Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone 410-278-5869 or 410-278-6748 Note my new email address: troy.d.kelley6.civ at mail.mil Classification: UNCLASSIFIED Caveats: NONE -------------- next part -------------- A non-text attachment was scrubbed... Name: smime.p7s Type: application/x-pkcs7-signature Size: 5619 bytes Desc: not available URL: From lorincz at inf.elte.hu Fri Mar 21 04:30:40 2014 From: lorincz at inf.elte.hu (Andras Lorincz) Date: Fri, 21 Mar 2014 08:30:40 +0000 Subject: Connectionists: how the brain works? In-Reply-To: <06FC2A13-B9C7-4D1B-A220-9A54B71B469B@uthscsa.edu> References: <5319F22D.80609@gmail.com> <53224F7F.9010406@cse.msu.edu> <532347AA.2020004@cse.msu.edu> <201403141939.s2EJavZB026551@mx0a-00164701.pphosted.com> <5329D8EA.8040901@cse.msu.edu> <3A546783-4BCB-493F-84FC-8030567EEA3C@uthscsa.edu> <201403192257.s2JMr87U028279@mx0b-00164701.pphosted.com> <545B99C1-A83E-421C-8CCC-D956073C2287@uthscsa.edu> <82a4a4bc8bbe49a9906d6dbac9ba314d@DB4PR06MB173.eurprd06.prod.outlook.com> <7127C02E-FECA-4A67-A0E8-E2E5FE7D4926@uthscsa.edu> , <06FC2A13-B9C7-4D1B-A220-9A54B71B469B@uthscsa.edu> Message-ID: <29072f6b8b6f4e6e86ac19a334e02376@DB4PR06MB173.eurprd06.prod.outlook.com> As a very crude analogy, and as a rough, actually bad description, consider that the brain is a Turing machine. It is made of two things: (1) regularities (the machine, the compressed part) and (2) randomness (the tape, the uncompressible part). It is your decision what to consider as the tape and what not. %--------------------------- The problem with this analogy is that the environment of the brain is not random and the brain learns the regularities of the environment. In turn, this Turing machine 'grows' (while the tape 'shrinks'). Andras http://people.inf.elte.hu/lorincz ________________________________ From: james bower Sent: Thursday, March 20, 2014 8:42 PM To: Tsvi Achler Cc: Andras Lorincz; Geoffrey Goodhill; connectionists at mailman.srv.cs.cmu.edu Subject: Re: Connectionists: how the brain works? actually it isn?t - however, it turns out for biology and neurobiology in particular the question of ?brittality? (no auto spell checker not brutality) is a serious one. I may have mentioned previously (my apologies) , but years ago a the Marine Biology Laboratory, I heard a neurobiologist give a presentation at the end of spending an entire summer characterizing all the ion channels in a particular cell in the sea slug (aplasia hermissenda). In his talk, he described those conductances that could explain the cells electrical behavior, and dismissed the majority of the ?small conductances? as likely just sloppiness in gene regulation. From the audience, I asked him if he had changed the temperature of the water bath (Aplasia live in tide pools). The next summer his talk was about the remarkable ability of the neuron to regulate its electrical behavior given changes in the ambient temperature in the tide pool. And guess what, those small conductances were responsible (there is a reason that voltage dependent conductances are highly temperature sensitive). So go ahead and model the sea slug as a round cylinder - unless you deal with the biological details you won?t see the engineering (or understand the function) I claim. Jim On Mar 20, 2014, at 2:00 PM, Tsvi Achler > wrote: Basically it is a definition of over-fitting. The fact large that models may be needed to capture neuron components such proteins and so on do not change the fact that too many extraneous parameters can make the model precise but brittle. -Tsvi On Thu, Mar 20, 2014 at 10:59 AM, james bower > wrote: Interesting definition - just to note, we build realistic biological models with hundreds to thousands of parameters to model a neuron - however, they are not ?free?. in fact, it is easier to get a fake model of a neuron to behave like a neuron than it is to make one designed to replicate the anatomy and physiology. Something that many in the physics / engineering worlds don?t realize. So, in fact, by the definition you use, abstract neuronal models which have a smaller number of essentially completely free parameters can?t use Ockham?s shaving cream as cover. :-) Again, to return to my old analogy - Kepler was forced BY THE DATA to use ellipses for the orbits of the planets. It is pretty clear he would rather have not. Newton on the other hand clearly benefited from the fact that the moons orbit is essentially circular in his first calculation of the inverse square law. In both cases, however, unlike Ptolomy, they were constructing physically realistic models. Doing so in Biology also requires you deal with complexity you would rather not (as I keep saying ad naus?? ) Jim On Mar 20, 2014, at 9:41 AM, Tsvi Achler > wrote: > I think an Ockham's razor principle can be used to find the most > optimal algorithm if it is interpreted to mean the model with the > least amount of free parameters that captures the most phenomena. > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.pdf > -Tsvi > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz > wrote: >> Ockham works here via compressing both the algorithm and the structure. >> Compressing the structure to stem cells means that the algorithm should >> describe the development, the working, and the time dependent structure of >> the brain. Not compressing the description of the structure of the evolved >> brain is a different problem since it saves the need for the description of >> the development, but the working. Understanding the structure and the >> working of one part of the brain requires the description of its >> communication that increases the complexity of the description. By the way, >> this holds for the whole brain, so we might have to include the body at >> least; a structural minimist may wish to start from the genetic code, use >> that hint and unfold the already compressed description. There are (many and >> different) todos 'outside' ... >> >> >> Andras >> >> >> >> >> . >> >> ________________________________ >> From: Connectionists > on >> behalf of james bower > >> Sent: Thursday, March 20, 2014 3:33 AM >> >> To: Geoffrey Goodhill >> Cc: connectionists at mailman.srv.cs.cmu.edu >> Subject: Re: Connectionists: how the brain works? >> >> Geoffrey, >> >> Nice addition to the discussion actually introducing an interesting angle on >> the question of brain organization (see below) As you note, reaction >> diffusion mechanisms and modeling have been quite successful in replicating >> patterns seen in biology - especially interesting I think is the modeling of >> patterns in slime molds, but also for very general pattern formation in >> embryology. However, more and more detailed analysis of what is diffusing, >> what is sensing what is diffusing, and what is reacting to substances once >> sensed -- all linked to complex patterns of gene regulation and expression >> have made it clear that actual embryological development is much much more >> complex, as Turing himself clearly anticipated, as the quote you cite pretty >> clearly indicates. Clearly a smart guy. But, I don't actually think that >> this is an application of Ochham's razor although it might appear to be >> after the fact. Just as Hodgkin and Huxley were not applying it either in >> their model of the action potential. Turing apparently guessed (based on a >> lot of work at the time on pattern formation with reaction diffusion) that >> such a mechanism might provide the natural basis for what embryos do. Thus, >> just like for Hodgkin and Huxley, his model resulted from a bio-physical >> insight, not an explicit attempt to build a stripped down model for its own >> sake. I seriously doubt that Turning would have claimed that he, or his >> models could more effectively do what biology actually does in forming an >> embrio, or substitute for the actual process. >> >> However, I think there is another interesting connection here to the >> discussion on modeling the brain. Almost certainly communication and >> organizational systems in early living beings were reaction diffusion based. >> This is still a dominant effect for many 'sensing' in small organisms. >> Perhaps, therefore, one can look at nervous systems as structures >> specifically developed to supersede reaction diffusion mechanisms, thus >> superseding this very 'natural' but complexity limited type of communication >> and organization. What this means, I believe, is that a simplified or >> abstracted physical or mathematical model of the brain explicitly violates >> the evolutionary pressures responsible for its structure. Its where the >> wires go, what the wires do, and what the receiving neuron does with the >> information that forms the basis for neural computation, multiplied by a >> very large number. And that is dependent on the actual physical structure >> of those elements. >> >> One more point about smart guys, as a young computational neurobiologist I >> questioned how insightful John von Neumann actually was because I was >> constantly hearing about a lecture he wrote (but didn't give) at Yale >> suggesting that dendrites and neurons might be digital ( John von Neumann's >> The Computer and the Brain. (New Haven/London: Yale Univesity Press, 1958.) >> Very clearly a not very insightful idea for a supposedly smart guy. It >> wasn't until a few years later, when I actually read the lecture - that I >> found out that he ends by stating that this idea is almost certainly wrong, >> given the likely nonlinearities in neuronal dendrites. So von Neumann >> didn't lack insight, the people who quoted him did. It is a remarkable fact >> that more than 60 years later, the majority of models of so called neurons >> built by engineers AND neurobiologists don't consider these nonlinearities. >> The point being the same point, to the Hopfield, Mead, Feynman list, we can >> now add Turing and von Neumann as suspecting that for understanding, >> biology and the nervous system must be dealt with in their full complexity. >> >> But thanks for the example from Turing - always nice to consider actual >> examples. :-) >> >> Jim >> >> >> >> >> >> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill > wrote: >> >> Hi All, >> >> A great example of successful Ockham-inspired biology is Alan Turing's model >> for pattern formation (spots, stripes etc) in embryology (The chemical basis >> of morphogenesis, Phil Trans Roy Soc, 1953). Turing introduced a physical >> mechanism for how inhomogeneous spatial patterns can arise in a biological >> system from a spatially homogeneous starting point, based on the diffusion >> of morphogens. The paper begins: >> >> "In this section a mathematical model of the growing embryo will be >> described. This model will be a simplification and an idealization, and >> consequently a falsification. It is to be hoped that the features retained >> for discussion are those of greatest importance in the present state of >> knowledge." >> >> The paper remained virtually uncited for its first 20 years following >> publication, but since then has amassed 8000 citations (Google Scholar). The >> subsequent discovery of huge quantities of molecular detail in biological >> pattern formation have only reinforced the importance of this relatively >> simple model, not because it explains every system, but because the >> overarching concepts it introduced have proved to be so fertile. >> >> Cheers, >> >> Geoff >> >> >> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: >> >> Ignoring the gross differences in circuitry between hippocampus and >> cerebellum, etc., is not erring on the side of simplicity, it is erring, >> period. Have you actually looked at a Cajal/Sxentagothai-style drawing of >> their circuitry? >> >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >> >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. Optimal >> compression requires a balance, and we can't compute what that balance is >> (all models are wrong). One thing we can say for sure is that we should err >> on the side of simplicity, and adding detail to theories before simpler >> explanations have failed is not Ockham's heuristic. That said it's still in >> the space of a Big Data fuzzy science approach, where we throw as much data >> from as many levels of analysis as we can come up with into a big pot and >> then construct a theory. The thing to keep in mind is that when we start >> pruning this model most of the details are going to disappear, because >> almost all of them are irrelevant. Indeed, the size of the description that >> includes all the details is almost infinite, whereas the length of the >> description that explains almost all the variance is extremely short, >> especially in comparison. This is why Ockham's razor is a good heuristic. It >> helps prevent us from wasting time on unnecessary details by suggesting that >> we only inquire as to the details once our existing simpler theory has >> failed to work. >> >> On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >> The brain uses a single architecture to do all brain functions we are aware >> of! It uses the same architecture to do vision, audition, motor, reasoning, >> decision making, motivation (including pain avoidance and pleasure seeking, >> novelty seeking, higher emotion, etc.). >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum were very >> different from each other. >> >> >> -------------- next part -------------- An HTML attachment was scrubbed... URL: From R.Borisyuk at plymouth.ac.uk Fri Mar 21 11:39:04 2014 From: R.Borisyuk at plymouth.ac.uk (Roman Borisyuk) Date: Fri, 21 Mar 2014 15:39:04 +0000 Subject: Connectionists: how the brain works? (UNCLASSIFIED) In-Reply-To: References: <51FDF3A2-717D-4399-9076-331EEA0FC45A@lehigh.edu> Message-ID: <7C21808200B7BB4EBAE3C051E15772F60D9CF9@TIS104.uopnet.plymouth.ac.uk> I agree that habituation effect ("orientation reaction/reflex") by E.N.Sokolov and O.S.Vinogradova is very important for understanding how the brain works. I would like to add to the nice discussion a reference to the last paper by Olga Vinogradova (she was a collaborator and co-author of E.N. Sokolov): Vinogradova O.S. (2001) Hippocampus as comparator: role of the two input and two output systems of the hippocampus in selection and registration of information. Hippocampus, 11 (5) 578-98. In this paper Olga summarises her experimental and theoretical findings on the role of the hippocampus (and theta-rhythm) for information processing in the brain. Roman Borisyuk, DSc, PhD Professor of Computational Neuroscience School of Computing and Mathematics University of Plymouth A224, Portland Sq Plymouth, PL4 8AA UK Phone: +44 (0) 1752 584949 E-mail: RBorisyuk at plymouth.ac.uk -----Original Message----- From: Connectionists [mailto:connectionists-bounces at mailman.srv.cs.cmu.edu] On Behalf Of Kelley, Troy D CIV (US) Sent: 21 March 2014 12:24 To: Mark H. Bickhard Cc: connectionists at mailman.srv.cs.cmu.edu Subject: Re: Connectionists: how the brain works? (UNCLASSIFIED) Classification: UNCLASSIFIED Caveats: NONE Yes, Mark, I would argue that habituation is anticipatory prediction. The neuron creates a model of the incoming stimulus and the neuron is essentially predicting that the next stimuli will be comparatively similar to the previous stimulus. If this prediction is met, the neuron habituates. That is a simple, low level, predictive model. -----Original Message----- From: Mark H. Bickhard [mailto:mhb0 at Lehigh.EDU] Sent: Thursday, March 20, 2014 5:28 PM To: Kelley, Troy D CIV (US) Cc: Tsvi Achler; Andras Lorincz; bower at uthscsa.edu; connectionists at mailman.srv.cs.cmu.edu Subject: Re: Connectionists: how the brain works? I would agree with the importance of Sokolov habituation, but there is more than one way to understand and generalize from this phenomenon: http://www.lehigh.edu/~mhb0/AnticipatoryBrain20Aug13.pdf Mark H. Bickhard Lehigh University 17 Memorial Drive East Bethlehem, PA 18015 mark at bickhard.name http://bickhard.ws/ On Mar 20, 2014, at 4:41 PM, Kelley, Troy D CIV (US) wrote: We have found that the habituation algorithm that Sokolov discovered way back in 1963 provides an useful place to start if one is trying to determine how the brain works. The algorithm, at the cellular level, is capable of determining novelty and generating implicit predictions - which it then habituates to. Additionally, it is capable of regenerating the original response when re-exposed to the same stimuli. All of these behaviors provide an excellent framework at the cellular level for explain all sorts of high level behaviors at the functional level. And it fits the Ockham's razor principle of using a single algorithm to explain a wide variety of explicit behavior. Troy D. Kelley RDRL-HRS-E Cognitive Robotics and Modeling Team Leader Human Research and Engineering Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone 410-278-5869 or 410-278-6748 Note my new email address: troy.d.kelley6.civ at mail.mil On 3/20/14 10:41 AM, "Tsvi Achler" wrote: > I think an Ockham's razor principle can be used to find the most > optimal algorithm if it is interpreted to mean the model with the > least amount of free parameters that captures the most phenomena. > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.p > df > -Tsvi > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz wrote: >> Ockham works here via compressing both the algorithm and the structure. >> Compressing the structure to stem cells means that the algorithm >> should describe the development, the working, and the time dependent >> structure of the brain. Not compressing the description of the >> structure of the evolved brain is a different problem since it saves >> the need for the description of the development, but the working. >> Understanding the structure and the working of one part of the brain >> requires the description of its communication that increases the >> complexity of the description. By the way, this holds for the whole >> brain, so we might have to include the body at least; a structural >> minimist may wish to start from the genetic code, use that hint and >> unfold the already compressed description. There are (many and >> different) todos 'outside' ... >> >> >> Andras >> >> >> >> >> . >> >> ________________________________ >> From: Connectionists >> on behalf of james bower >> Sent: Thursday, March 20, 2014 3:33 AM >> >> To: Geoffrey Goodhill >> Cc: connectionists at mailman.srv.cs.cmu.edu >> Subject: Re: Connectionists: how the brain works? >> >> Geoffrey, >> >> Nice addition to the discussion actually introducing an interesting >> angle on the question of brain organization (see below) As you note, >> reaction diffusion mechanisms and modeling have been quite successful >> in replicating patterns seen in biology - especially interesting I >> think is the modeling of patterns in slime molds, but also for very >> general pattern formation in embryology. However, more and more >> detailed analysis of what is diffusing, what is sensing what is >> diffusing, and what is reacting to substances once sensed -- all >> linked to complex patterns of gene regulation and expression have >> made it clear that actual embryological development is much much more complex, as Turing himself clearly anticipated, as the quote you cite pretty >> clearly indicates. Clearly a smart guy. But, I don't actually think that >> this is an application of Ochham's razor although it might appear to >> be after the fact. Just as Hodgkin and Huxley were not applying it either in >> their model of the action potential. Turing apparently guessed (based on a >> lot of work at the time on pattern formation with reaction diffusion) >> that such a mechanism might provide the natural basis for what >> embryos do. Thus, just like for Hodgkin and Huxley, his model >> resulted from a bio-physical insight, not an explicit attempt to >> build a stripped down model for its own sake. I seriously doubt >> that Turning would have claimed that he, or his models could more >> effectively do what biology actually does in forming an embrio, or substitute for the actual process. >> >> However, I think there is another interesting connection here to the >> discussion on modeling the brain. Almost certainly communication and >> organizational systems in early living beings were reaction diffusion based. >> This is still a dominant effect for many 'sensing' in small organisms. >> Perhaps, therefore, one can look at nervous systems as structures >> specifically developed to supersede reaction diffusion mechanisms, >> thus superseding this very 'natural' but complexity limited type of >> communication and organization. What this means, I believe, is that >> a simplified or abstracted physical or mathematical model of the >> brain explicitly violates the evolutionary pressures responsible for >> its structure. Its where the wires go, what the wires do, and what >> the receiving neuron does with the information that forms the basis >> for neural computation, multiplied by a very large number. And that >> is dependent on the actual physical structure of those elements. >> >> One more point about smart guys, as a young computational >> neurobiologist I questioned how insightful John von Neumann actually >> was because I was constantly hearing about a lecture he wrote (but >> didn't give) at Yale suggesting that dendrites and neurons might be >> digital ( John von Neumann's The Computer and the Brain. (New >> Haven/London: Yale Univesity Press, 1958.) Very clearly a not very >> insightful idea for a supposedly smart guy. It wasn't until a few >> years later, when I actually read the lecture - that I found out that >> he ends by stating that this idea is almost certainly wrong, given >> the likely nonlinearities in neuronal dendrites. So von Neumann >> didn't lack insight, the people who quoted him did. It is a >> remarkable fact that more than 60 years later, the majority of models of so called neurons built by engineers AND neurobiologists don't consider these nonlinearities. >> The point being the same point, to the Hopfield, Mead, Feynman list, >> we can now add Turing and von Neumann as suspecting that for >> understanding, biology and the nervous system must be dealt with in their full complexity. >> >> But thanks for the example from Turing - always nice to consider actual >> examples. :-) >> >> Jim >> >> >> >> >> >> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill wrote: >> >> Hi All, >> >> A great example of successful Ockham-inspired biology is Alan >> Turing's model for pattern formation (spots, stripes etc) in >> embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, >> 1953). Turing introduced a physical mechanism for how inhomogeneous >> spatial patterns can arise in a biological system from a spatially >> homogeneous starting point, based on the diffusion of morphogens. The paper begins: >> >> "In this section a mathematical model of the growing embryo will be >> described. This model will be a simplification and an idealization, >> and consequently a falsification. It is to be hoped that the features >> retained for discussion are those of greatest importance in the >> present state of knowledge." >> >> The paper remained virtually uncited for its first 20 years following >> publication, but since then has amassed 8000 citations (Google >> Scholar). The subsequent discovery of huge quantities of molecular >> detail in biological pattern formation have only reinforced the >> importance of this relatively simple model, not because it explains >> every system, but because the overarching concepts it introduced have proved to be so fertile. >> >> Cheers, >> >> Geoff >> >> >> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: >> >> Ignoring the gross differences in circuitry between hippocampus and >> cerebellum, etc., is not erring on the side of simplicity, it is >> erring, period. Have you actually looked at a >> Cajal/Sxentagothai-style drawing of their circuitry? >> >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >> >> Hi Jim, >> >> Focusing too much on the details is risky in and of itself. Optimal >> compression requires a balance, and we can't compute what that >> balance is (all models are wrong). One thing we can say for sure is >> that we should err on the side of simplicity, and adding detail to >> theories before simpler explanations have failed is not Ockham's >> heuristic. That said it's still in the space of a Big Data fuzzy >> science approach, where we throw as much data from as many levels of >> analysis as we can come up with into a big pot and then construct a >> theory. The thing to keep in mind is that when we start pruning this >> model most of the details are going to disappear, because almost all >> of them are irrelevant. Indeed, the size of the description that >> includes all the details is almost infinite, whereas the length of >> the description that explains almost all the variance is extremely >> short, especially in comparison. This is why Ockham's razor is a good >> heuristic. It helps prevent us from wasting time on unnecessary >> details by suggesting that we only inquire as to the details once our existing simpler theory has failed to work. >> >> On 3/14/14 3:40 PM, Michael Arbib wrote: >> >> At 11:17 AM 3/14/2014, Juyang Weng wrote: >> >> The brain uses a single architecture to do all brain functions we are >> aware of! It uses the same architecture to do vision, audition, >> motor, reasoning, decision making, motivation (including pain >> avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). >> >> >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum >> were very different from each other. >> >> >> Troy D. Kelley RDRL-HRS-E Cognitive Robotics and Modeling Team Leader Human Research and Engineering Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone 410-278-5869 or 410-278-6748 Note my new email address: troy.d.kelley6.civ at mail.mil Classification: UNCLASSIFIED Caveats: NONE ________________________________ [http://www.plymouth.ac.uk/images/email_footer.gif] This email and any files with it are confidential and intended solely for the use of the recipient to whom it is addressed. If you are not the intended recipient then copying, distribution or other use of the information contained is strictly prohibited and you should not rely on it. If you have received this email in error please let the sender know immediately and delete it from your system(s). Internet emails are not necessarily secure. While we take every care, Plymouth University accepts no responsibility for viruses and it is your responsibility to scan emails and their attachments. Plymouth University does not accept responsibility for any changes made after it was sent. Nothing in this email or its attachments constitutes an order for goods or services unless accompanied by an official order form. From jason at cs.jhu.edu Fri Mar 21 12:02:43 2014 From: jason at cs.jhu.edu (Jason Eisner) Date: Fri, 21 Mar 2014 12:02:43 -0400 Subject: Connectionists: endowed, tenured chair in Computational Healthcare at Johns Hopkins Message-ID: Dear connectionists (post #2 of 2): ML researchers are welcome candidates for this endowed professorship in *computational healthcare*. Please consider applying. Or alerting your favorite distinguished researcher who is using ML on health data. This is one of 50 new chairs made possible by a recent $350 million gift to JHU. Some will be ML-related, and I'll separately post the one on *computational cognitive science*. Questions welcome. Bloomberg Distinguished Professorship in *Computational Healthcare* *Joint Search by the Johns Hopkins University School of Medicine and School of Engineering* The Johns Hopkins University seeks an internationally recognized leader as a tenured, endowed Bloomberg Distinguished Professor in the emerging area of *computational healthcare*. We seek an individual whose research agenda in healthcare and data science will help bring together the University's Whiting School of Engineering and School of Medicine through their shared interests in computational modeling of health data and its applications to improve health care delivery. This leader will pioneer new directions, interact with existing collaborative research efforts, and help guide additional faculty growth in this area. This position is one of 50 new Bloomberg Distinguished Professors whose mission will be to pursue transformative strategies to enable Johns Hopkins to lead in the development of solutions to major societal problems. Bloomberg Distinguished Professors will enhance significantly the university's longstanding commitment to research, teaching and service that spans disciplinary boundaries and Schools. The explosion of data such as neuroimages, DNA sequences, electronic medical records, and physiological signals collected from individual and populations of patients raises the prospect of discovering optimal approaches to individualized health care. The Hopkins Individualized Health Initiative (INhealth) is a focus of the University's current capital campaign. It seeks to bring together strengths in biomedical sciences, data science, and engineering at Johns Hopkins University, Johns Hopkins Health System, and the Applied Physics Laboratory (APL) to: - *discover* new computational methods for defining, measuring, and communicating each person's unique health state and trajectory; - *apply* these methods to produce better health outcomes at more affordable costs. This search in computational healthcare is one of several INHealth-related searches that will recruit new Bloomberg Distinguished Professors to drive progress. These professors will also benefit from the University's strategic initiative in The Science of Learning, and the Whiting School of Engineering research thrust in Leveraging Data to Knowledge. Two other INHealth-related searches are underway in the health information sciences domain (statistical genomics and health informatics). Bloomberg Distinguished Professors will hold formal tenured appointments in departments of two or more schools of the University, and will participate fully in the research, teaching, and service missions of their departments, including undergraduate and graduate education. The Johns Hopkins University School of Medicine and Whiting School of Engineering are partners in this computational healthcare search. Supporting departments include: the Department of Computer Science in the Whiting School of Engineering; and the Departments of Biomedical Engineering, Emergency Medicine, Medicine, and Radiology in the School of Medicine. The primary academic appointment in the Whiting School of Engineering will be in the Department of Computer Science. The ideal candidate will satisfy the following criteria: international reputation and major research and teaching accomplishments in both computing- and health-related fields such as biomedical informatics, computer science, data-intensive science, statistics, machine learning, computational modeling; track record of translating computational approaches into clinical applications within academic health centers or other health care delivery systems; strong record of extramural funding. Applicants should send their Curriculum Vitae as a single PDF to Stephanie Steele, Search Coordinator, The Johns Hopkins Institute for Computational Medicine, ssteele at jhu.edu. Applications should be received no later than *April 30, 2014*. Additional information is available at http://cs.jhu.edu/BDP. The Johns Hopkins University is committed to enhancing the diversity of its faculty, strongly encourages applications from women and minorities, and is an EEO/AA employer. -------------- next part -------------- An HTML attachment was scrubbed... URL: From jason at cs.jhu.edu Fri Mar 21 12:02:16 2014 From: jason at cs.jhu.edu (Jason Eisner) Date: Fri, 21 Mar 2014 12:02:16 -0400 Subject: Connectionists: endowed, tenured chair in Computational Cognitive Science at Johns Hopkins Message-ID: Dear connectionists (post #1 of 2): ML researchers are welcome candidates for this endowed professorship in *computational cognitive science*. Please consider applying. Or alert your favorite distinguished researcher who combines interests in artificial and natural intelligence. This is one of 50 new chairs made possible by a recent $350 million gift to JHU. Some will be ML-related, and I'll separately post the one on *ML for healthcare*. Questions welcome. Bloomberg Distinguished Professorship in *Computational Cognitive Science* *Joint Search by the Johns Hopkins University School of Arts and Sciences and School of Engineering* The Johns Hopkins University seeks an internationally recognized leader as a tenured, endowed Bloomberg Distinguished Professor in the area of *computational cognitive science*. We seek an individual whose research agenda in computation and cognition will further bring together the University's Whiting School of Engineering and Krieger School of Arts & Sciences through their shared interests in computational modeling and human cognition. This leader will pioneer new directions, interact with existing collaborative research efforts, and help guide additional faculty growth in this major intellectual area. This position is one of 50 new Bloomberg Distinguished Professorships, designated for outstanding scholars to carry out interdisciplinary research and teaching in areas identified for significant growth at the University. New advances in computational modeling and machine learning, along with new sources of behavioral data, are opening up opportunities to build more complete and accurate models of intelligent behavior. The Johns Hopkins initiative in the Science of Learning (SoL) is a focus of the University's current capital campaign. It seeks to bring together the university's strengths in machine learning, cognitive science, neuroscience, education, and domain-specific areas such as language and vision, in order to understand the nature of learning at all levels of scientific inquiry (from synaptic changes to educational strategies). This search in computational cognitive science is one of several SoL-related searches that will recruit new Bloomberg Distinguished Professors and other faculty to drive progress on neural, cognitive, and computational approaches to learning. These professors will also benefit from the Whiting School of Engineering's strategic initiative in Leveraging Data to Knowledge. Bloomberg Distinguished Professors will hold formal tenured appointments in departments of two or more schools of the university--in this case, in the Department of Computer Science and the Department of Cognitive Science . They will participate fully in the research, teaching, and service missions of their departments, including undergraduate and graduate education. For this position, the ideal candidate will have an international reputation based on research and teaching accomplishments that involve both computational modeling and human data. The candidate will be suitable for a tenured appointment at Johns Hopkins University. The appointee will enjoy many opportunities to engage with other world-class units in the Science of Learning initiative, including the Center for Language and Speech Processing , the Center for Imaging Science , the Machine Learning Group, the Vision Sciences Group , the Center of Excellence in Human Language Technology , the Psychological and Brain Sciences Department , the School of Education, and the renowned Hopkins neuroscience community that includes the Mind/Brain Institute and the School of Medicine's Department of Neurology. Applicants should send their Curriculum Vitae (ideally including links to publications) as a single PDF file to compcogsci-bdp-search at jhu.edu. Applications will be reviewed beginning *April 30, 2014* although later applications may be considered. Additional information is available at http://cs.jhu.edu/BDP. The Johns Hopkins University is committed to enhancing the diversity of its faculty, strongly encourages applications from women and minorities, and is an EEO/AA employer. -------------- next part -------------- An HTML attachment was scrubbed... URL: From achler at gmail.com Fri Mar 21 22:29:32 2014 From: achler at gmail.com (Tsvi Achler) Date: Fri, 21 Mar 2014 19:29:32 -0700 Subject: Connectionists: how the brain works? (UNCLASSIFIED) In-Reply-To: References: <51FDF3A2-717D-4399-9076-331EEA0FC45A@lehigh.edu> Message-ID: Sorry for the length of this response but I wanted to go into some detail here. I see the habituation paradigm as somewhat analogous to surprise and measurement of error during recognition. I can think of a few mathematical Neural Network classifiers that can generate an internal pattern for match during recognition to calculate this habituation/surprise orientation. Feedforward networks definitely will not work because they don't recall the internal stimulus very well. One option is adaptive resonance (which I assume you use), but it cycles through the patterns one at a time and requires complex signals indicating when to stop, compare, and cycle. I assume Juyang's DN can also do something similar but I suspect it also must cycle since it also has lateral inhibition. Bidirectional Associative Memories (BAM) may also be used. Others such as Bayes networks and free-energy principle can used, although they are not as easily translatable to neural networks. Another option is a network like mine which does not have lateral connections but also generates internal patterns. The advantage is that it can also generate mixtures of patterns at once, does not cycle through individual patterns, does not require signals associated with cycling, and can be shown mathematically to be analogous to feedforward networks. The error signal it produces can be used for an orientation reflex or what I rather call attention. It is essential for recognition and planning. I would be happy to give a talk on this and collaborate on a rigorous comparison. Indeed it is important to look at models other than those using feedforward connections during recognition. Sincerely, -Tsvi On Mar 21, 2014 5:25 AM, "Kelley, Troy D CIV (US)" wrote: > > Classification: UNCLASSIFIED > Caveats: NONE > > Yes, Mark, I would argue that habituation is anticipatory prediction. The > neuron creates a model of the incoming stimulus and the neuron is > essentially predicting that the next stimuli will be comparatively similar > to the previous stimulus. If this prediction is met, the neuron habituates. > That is a simple, low level, predictive model. > > -----Original Message----- > From: Mark H. Bickhard [mailto:mhb0 at Lehigh.EDU] > Sent: Thursday, March 20, 2014 5:28 PM > To: Kelley, Troy D CIV (US) > Cc: Tsvi Achler; Andras Lorincz; bower at uthscsa.edu; > connectionists at mailman.srv.cs.cmu.edu > Subject: Re: Connectionists: how the brain works? > > I would agree with the importance of Sokolov habituation, but there is more > than one way to understand and generalize from this phenomenon: > > http://www.lehigh.edu/~mhb0/AnticipatoryBrain20Aug13.pdf > > Mark H. Bickhard > Lehigh University > 17 Memorial Drive East > Bethlehem, PA 18015 > mark at bickhard.name > http://bickhard.ws/ > > On Mar 20, 2014, at 4:41 PM, Kelley, Troy D CIV (US) wrote: > > We have found that the habituation algorithm that Sokolov discovered way > back in 1963 provides an useful place to start if one is trying to determine > how the brain works. The algorithm, at the cellular level, is capable of > determining novelty and generating implicit predictions - which it then > habituates to. Additionally, it is capable of regenerating the original > response when re-exposed to the same stimuli. All of these behaviors > provide an excellent framework at the cellular level for explain all sorts > of high level behaviors at the functional level. And it fits the Ockham's > razor principle of using a single algorithm to explain a wide variety of > explicit behavior. > > Troy D. Kelley > RDRL-HRS-E > Cognitive Robotics and Modeling Team Leader Human Research and Engineering > Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone > 410-278-5869 or 410-278-6748 Note my new email address: > troy.d.kelley6.civ at mail.mil > > > > > > On 3/20/14 10:41 AM, "Tsvi Achler" wrote: > > > I think an Ockham's razor principle can be used to find the most > > optimal algorithm if it is interpreted to mean the model with the > > least amount of free parameters that captures the most phenomena. > > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.p > > df > > -Tsvi > > > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz > wrote: > >> Ockham works here via compressing both the algorithm and the structure. > >> Compressing the structure to stem cells means that the algorithm > >> should describe the development, the working, and the time dependent > >> structure of the brain. Not compressing the description of the > >> structure of the evolved brain is a different problem since it saves > >> the need for the description of the development, but the working. > >> Understanding the structure and the working of one part of the brain > >> requires the description of its communication that increases the > >> complexity of the description. By the way, this holds for the whole > >> brain, so we might have to include the body at least; a structural > >> minimist may wish to start from the genetic code, use that hint and > >> unfold the already compressed description. There are (many and > >> different) todos 'outside' ... > >> > >> > >> Andras > >> > >> > >> > >> > >> . > >> > >> ________________________________ > >> From: Connectionists > >> on behalf of james bower > >> Sent: Thursday, March 20, 2014 3:33 AM > >> > >> To: Geoffrey Goodhill > >> Cc: connectionists at mailman.srv.cs.cmu.edu > >> Subject: Re: Connectionists: how the brain works? > >> > >> Geoffrey, > >> > >> Nice addition to the discussion actually introducing an interesting > >> angle on the question of brain organization (see below) As you note, > >> reaction diffusion mechanisms and modeling have been quite successful > >> in replicating patterns seen in biology - especially interesting I > >> think is the modeling of patterns in slime molds, but also for very > >> general pattern formation in embryology. However, more and more > >> detailed analysis of what is diffusing, what is sensing what is > >> diffusing, and what is reacting to substances once sensed -- all > >> linked to complex patterns of gene regulation and expression have > >> made it clear that actual embryological development is much much more > complex, as Turing himself clearly anticipated, as the quote you cite pretty > >> clearly indicates. Clearly a smart guy. But, I don't actually think > that > >> this is an application of Ochham's razor although it might appear to > >> be after the fact. Just as Hodgkin and Huxley were not applying it > either in > >> their model of the action potential. Turing apparently guessed (based > on a > >> lot of work at the time on pattern formation with reaction diffusion) > >> that such a mechanism might provide the natural basis for what > >> embryos do. Thus, just like for Hodgkin and Huxley, his model > >> resulted from a bio-physical insight, not an explicit attempt to > >> build a stripped down model for its own sake. I seriously doubt > >> that Turning would have claimed that he, or his models could more > >> effectively do what biology actually does in forming an embrio, or > substitute for the actual process. > >> > >> However, I think there is another interesting connection here to the > >> discussion on modeling the brain. Almost certainly communication and > >> organizational systems in early living beings were reaction diffusion > based. > >> This is still a dominant effect for many 'sensing' in small organisms. > >> Perhaps, therefore, one can look at nervous systems as structures > >> specifically developed to supersede reaction diffusion mechanisms, > >> thus superseding this very 'natural' but complexity limited type of > >> communication and organization. What this means, I believe, is that > >> a simplified or abstracted physical or mathematical model of the > >> brain explicitly violates the evolutionary pressures responsible for > >> its structure. Its where the wires go, what the wires do, and what > >> the receiving neuron does with the information that forms the basis > >> for neural computation, multiplied by a very large number. And that > >> is dependent on the actual physical structure of those elements. > >> > >> One more point about smart guys, as a young computational > >> neurobiologist I questioned how insightful John von Neumann actually > >> was because I was constantly hearing about a lecture he wrote (but > >> didn't give) at Yale suggesting that dendrites and neurons might be > >> digital ( John von Neumann's The Computer and the Brain. (New > >> Haven/London: Yale Univesity Press, 1958.) Very clearly a not very > >> insightful idea for a supposedly smart guy. It wasn't until a few > >> years later, when I actually read the lecture - that I found out that > >> he ends by stating that this idea is almost certainly wrong, given > >> the likely nonlinearities in neuronal dendrites. So von Neumann > >> didn't lack insight, the people who quoted him did. It is a > >> remarkable fact that more than 60 years later, the majority of models of > so called neurons built by engineers AND neurobiologists don't consider > these nonlinearities. > >> The point being the same point, to the Hopfield, Mead, Feynman list, > >> we can now add Turing and von Neumann as suspecting that for > >> understanding, biology and the nervous system must be dealt with in their > full complexity. > >> > >> But thanks for the example from Turing - always nice to consider actual > >> examples. :-) > >> > >> Jim > >> > >> > >> > >> > >> > >> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill > wrote: > >> > >> Hi All, > >> > >> A great example of successful Ockham-inspired biology is Alan > >> Turing's model for pattern formation (spots, stripes etc) in > >> embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, > >> 1953). Turing introduced a physical mechanism for how inhomogeneous > >> spatial patterns can arise in a biological system from a spatially > >> homogeneous starting point, based on the diffusion of morphogens. The > paper begins: > >> > >> "In this section a mathematical model of the growing embryo will be > >> described. This model will be a simplification and an idealization, > >> and consequently a falsification. It is to be hoped that the features > >> retained for discussion are those of greatest importance in the > >> present state of knowledge." > >> > >> The paper remained virtually uncited for its first 20 years following > >> publication, but since then has amassed 8000 citations (Google > >> Scholar). The subsequent discovery of huge quantities of molecular > >> detail in biological pattern formation have only reinforced the > >> importance of this relatively simple model, not because it explains > >> every system, but because the overarching concepts it introduced have > proved to be so fertile. > >> > >> Cheers, > >> > >> Geoff > >> > >> > >> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: > >> > >> Ignoring the gross differences in circuitry between hippocampus and > >> cerebellum, etc., is not erring on the side of simplicity, it is > >> erring, period. Have you actually looked at a > >> Cajal/Sxentagothai-style drawing of their circuitry? > >> > >> At 01:07 PM 3/19/2014, Brian J Mingus wrote: > >> > >> Hi Jim, > >> > >> Focusing too much on the details is risky in and of itself. Optimal > >> compression requires a balance, and we can't compute what that > >> balance is (all models are wrong). One thing we can say for sure is > >> that we should err on the side of simplicity, and adding detail to > >> theories before simpler explanations have failed is not Ockham's > >> heuristic. That said it's still in the space of a Big Data fuzzy > >> science approach, where we throw as much data from as many levels of > >> analysis as we can come up with into a big pot and then construct a > >> theory. The thing to keep in mind is that when we start pruning this > >> model most of the details are going to disappear, because almost all > >> of them are irrelevant. Indeed, the size of the description that > >> includes all the details is almost infinite, whereas the length of > >> the description that explains almost all the variance is extremely > >> short, especially in comparison. This is why Ockham's razor is a good > >> heuristic. It helps prevent us from wasting time on unnecessary > >> details by suggesting that we only inquire as to the details once our > existing simpler theory has failed to work. > >> > >> On 3/14/14 3:40 PM, Michael Arbib wrote: > >> > >> At 11:17 AM 3/14/2014, Juyang Weng wrote: > >> > >> The brain uses a single architecture to do all brain functions we are > >> aware of! It uses the same architecture to do vision, audition, > >> motor, reasoning, decision making, motivation (including pain > >> avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). > >> > >> > >> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum > >> were very different from each other. > >> > >> > >> > > Troy D. Kelley > RDRL-HRS-E > Cognitive Robotics and Modeling Team Leader Human Research and Engineering > Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone > 410-278-5869 or 410-278-6748 Note my new email address: > troy.d.kelley6.civ at mail.mil > > > > Classification: UNCLASSIFIED > Caveats: NONE > > From steve at cns.bu.edu Sat Mar 22 07:07:50 2014 From: steve at cns.bu.edu (Stephen Grossberg) Date: Sat, 22 Mar 2014 07:07:50 -0400 Subject: Connectionists: how the brain works? (UNCLASSIFIED) In-Reply-To: References: <51FDF3A2-717D-4399-9076-331EEA0FC45A@lehigh.edu> Message-ID: Dear Tsvi, You mention Adaptive Resonance below and suggest that it "requires complex signals indicating when to stop, compare, and cycle". That is not correct. ART uses a simple measure of pattern mismatch. Moreover, psychological, neurophysiological, anatomical, and ERP data support the operations that it models during hypothesis testing and memory search. ART predicted various of these data before they were collected. If you would like to pursue this further, see http://cns.bu.edu/~steve/ART.pdf for a recent heuristic review. Best, Steve On Mar 21, 2014, at 10:29 PM, Tsvi Achler wrote: > Sorry for the length of this response but I wanted to go into some > detail here. > > I see the habituation paradigm as somewhat analogous to surprise and > measurement of error during recognition. I can think of a few > mathematical Neural Network classifiers that can generate an internal > pattern for match during recognition to calculate this > habituation/surprise orientation. Feedforward networks definitely > will not work because they don't recall the internal stimulus very > well. One option is adaptive resonance (which I assume you use), but > it cycles through the patterns one at a time and requires complex > signals indicating when to stop, compare, and cycle. I assume > Juyang's DN can also do something similar but I suspect it also must > cycle since it also has lateral inhibition. Bidirectional Associative > Memories (BAM) may also be used. Others such as Bayes networks and > free-energy principle can used, although they are not as easily > translatable to neural networks. > > Another option is a network like mine which does not have lateral > connections but also generates internal patterns. The advantage is > that it can also generate mixtures of patterns at once, does not cycle > through individual patterns, does not require signals associated with > cycling, and can be shown mathematically to be analogous to > feedforward networks. The error signal it produces can be used for an > orientation reflex or what I rather call attention. It is essential > for recognition and planning. > > I would be happy to give a talk on this and collaborate on a rigorous > comparison. Indeed it is important to look at models other than those > using feedforward connections during recognition. > > Sincerely, > > -Tsvi > > > > > On Mar 21, 2014 5:25 AM, "Kelley, Troy D CIV (US)" > wrote: >> >> Classification: UNCLASSIFIED >> Caveats: NONE >> >> Yes, Mark, I would argue that habituation is anticipatory prediction. The >> neuron creates a model of the incoming stimulus and the neuron is >> essentially predicting that the next stimuli will be comparatively similar >> to the previous stimulus. If this prediction is met, the neuron habituates. >> That is a simple, low level, predictive model. >> >> -----Original Message----- >> From: Mark H. Bickhard [mailto:mhb0 at Lehigh.EDU] >> Sent: Thursday, March 20, 2014 5:28 PM >> To: Kelley, Troy D CIV (US) >> Cc: Tsvi Achler; Andras Lorincz; bower at uthscsa.edu; >> connectionists at mailman.srv.cs.cmu.edu >> Subject: Re: Connectionists: how the brain works? >> >> I would agree with the importance of Sokolov habituation, but there is more >> than one way to understand and generalize from this phenomenon: >> >> http://www.lehigh.edu/~mhb0/AnticipatoryBrain20Aug13.pdf >> >> Mark H. Bickhard >> Lehigh University >> 17 Memorial Drive East >> Bethlehem, PA 18015 >> mark at bickhard.name >> http://bickhard.ws/ >> >> On Mar 20, 2014, at 4:41 PM, Kelley, Troy D CIV (US) wrote: >> >> We have found that the habituation algorithm that Sokolov discovered way >> back in 1963 provides an useful place to start if one is trying to determine >> how the brain works. The algorithm, at the cellular level, is capable of >> determining novelty and generating implicit predictions - which it then >> habituates to. Additionally, it is capable of regenerating the original >> response when re-exposed to the same stimuli. All of these behaviors >> provide an excellent framework at the cellular level for explain all sorts >> of high level behaviors at the functional level. And it fits the Ockham's >> razor principle of using a single algorithm to explain a wide variety of >> explicit behavior. >> >> Troy D. Kelley >> RDRL-HRS-E >> Cognitive Robotics and Modeling Team Leader Human Research and Engineering >> Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone >> 410-278-5869 or 410-278-6748 Note my new email address: >> troy.d.kelley6.civ at mail.mil >> >> >> >> >> >> On 3/20/14 10:41 AM, "Tsvi Achler" wrote: >> >>> I think an Ockham's razor principle can be used to find the most >>> optimal algorithm if it is interpreted to mean the model with the >>> least amount of free parameters that captures the most phenomena. >>> http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.p >>> df >>> -Tsvi >>> >>> On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz >> wrote: >>>> Ockham works here via compressing both the algorithm and the structure. >>>> Compressing the structure to stem cells means that the algorithm >>>> should describe the development, the working, and the time dependent >>>> structure of the brain. Not compressing the description of the >>>> structure of the evolved brain is a different problem since it saves >>>> the need for the description of the development, but the working. >>>> Understanding the structure and the working of one part of the brain >>>> requires the description of its communication that increases the >>>> complexity of the description. By the way, this holds for the whole >>>> brain, so we might have to include the body at least; a structural >>>> minimist may wish to start from the genetic code, use that hint and >>>> unfold the already compressed description. There are (many and >>>> different) todos 'outside' ... >>>> >>>> >>>> Andras >>>> >>>> >>>> >>>> >>>> . >>>> >>>> ________________________________ >>>> From: Connectionists >>>> on behalf of james bower >>>> Sent: Thursday, March 20, 2014 3:33 AM >>>> >>>> To: Geoffrey Goodhill >>>> Cc: connectionists at mailman.srv.cs.cmu.edu >>>> Subject: Re: Connectionists: how the brain works? >>>> >>>> Geoffrey, >>>> >>>> Nice addition to the discussion actually introducing an interesting >>>> angle on the question of brain organization (see below) As you note, >>>> reaction diffusion mechanisms and modeling have been quite successful >>>> in replicating patterns seen in biology - especially interesting I >>>> think is the modeling of patterns in slime molds, but also for very >>>> general pattern formation in embryology. However, more and more >>>> detailed analysis of what is diffusing, what is sensing what is >>>> diffusing, and what is reacting to substances once sensed -- all >>>> linked to complex patterns of gene regulation and expression have >>>> made it clear that actual embryological development is much much more >> complex, as Turing himself clearly anticipated, as the quote you cite pretty >>>> clearly indicates. Clearly a smart guy. But, I don't actually think >> that >>>> this is an application of Ochham's razor although it might appear to >>>> be after the fact. Just as Hodgkin and Huxley were not applying it >> either in >>>> their model of the action potential. Turing apparently guessed (based >> on a >>>> lot of work at the time on pattern formation with reaction diffusion) >>>> that such a mechanism might provide the natural basis for what >>>> embryos do. Thus, just like for Hodgkin and Huxley, his model >>>> resulted from a bio-physical insight, not an explicit attempt to >>>> build a stripped down model for its own sake. I seriously doubt >>>> that Turning would have claimed that he, or his models could more >>>> effectively do what biology actually does in forming an embrio, or >> substitute for the actual process. >>>> >>>> However, I think there is another interesting connection here to the >>>> discussion on modeling the brain. Almost certainly communication and >>>> organizational systems in early living beings were reaction diffusion >> based. >>>> This is still a dominant effect for many 'sensing' in small organisms. >>>> Perhaps, therefore, one can look at nervous systems as structures >>>> specifically developed to supersede reaction diffusion mechanisms, >>>> thus superseding this very 'natural' but complexity limited type of >>>> communication and organization. What this means, I believe, is that >>>> a simplified or abstracted physical or mathematical model of the >>>> brain explicitly violates the evolutionary pressures responsible for >>>> its structure. Its where the wires go, what the wires do, and what >>>> the receiving neuron does with the information that forms the basis >>>> for neural computation, multiplied by a very large number. And that >>>> is dependent on the actual physical structure of those elements. >>>> >>>> One more point about smart guys, as a young computational >>>> neurobiologist I questioned how insightful John von Neumann actually >>>> was because I was constantly hearing about a lecture he wrote (but >>>> didn't give) at Yale suggesting that dendrites and neurons might be >>>> digital ( John von Neumann's The Computer and the Brain. (New >>>> Haven/London: Yale Univesity Press, 1958.) Very clearly a not very >>>> insightful idea for a supposedly smart guy. It wasn't until a few >>>> years later, when I actually read the lecture - that I found out that >>>> he ends by stating that this idea is almost certainly wrong, given >>>> the likely nonlinearities in neuronal dendrites. So von Neumann >>>> didn't lack insight, the people who quoted him did. It is a >>>> remarkable fact that more than 60 years later, the majority of models of >> so called neurons built by engineers AND neurobiologists don't consider >> these nonlinearities. >>>> The point being the same point, to the Hopfield, Mead, Feynman list, >>>> we can now add Turing and von Neumann as suspecting that for >>>> understanding, biology and the nervous system must be dealt with in their >> full complexity. >>>> >>>> But thanks for the example from Turing - always nice to consider actual >>>> examples. :-) >>>> >>>> Jim >>>> >>>> >>>> >>>> >>>> >>>> On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill >> wrote: >>>> >>>> Hi All, >>>> >>>> A great example of successful Ockham-inspired biology is Alan >>>> Turing's model for pattern formation (spots, stripes etc) in >>>> embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, >>>> 1953). Turing introduced a physical mechanism for how inhomogeneous >>>> spatial patterns can arise in a biological system from a spatially >>>> homogeneous starting point, based on the diffusion of morphogens. The >> paper begins: >>>> >>>> "In this section a mathematical model of the growing embryo will be >>>> described. This model will be a simplification and an idealization, >>>> and consequently a falsification. It is to be hoped that the features >>>> retained for discussion are those of greatest importance in the >>>> present state of knowledge." >>>> >>>> The paper remained virtually uncited for its first 20 years following >>>> publication, but since then has amassed 8000 citations (Google >>>> Scholar). The subsequent discovery of huge quantities of molecular >>>> detail in biological pattern formation have only reinforced the >>>> importance of this relatively simple model, not because it explains >>>> every system, but because the overarching concepts it introduced have >> proved to be so fertile. >>>> >>>> Cheers, >>>> >>>> Geoff >>>> >>>> >>>> On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: >>>> >>>> Ignoring the gross differences in circuitry between hippocampus and >>>> cerebellum, etc., is not erring on the side of simplicity, it is >>>> erring, period. Have you actually looked at a >>>> Cajal/Sxentagothai-style drawing of their circuitry? >>>> >>>> At 01:07 PM 3/19/2014, Brian J Mingus wrote: >>>> >>>> Hi Jim, >>>> >>>> Focusing too much on the details is risky in and of itself. Optimal >>>> compression requires a balance, and we can't compute what that >>>> balance is (all models are wrong). One thing we can say for sure is >>>> that we should err on the side of simplicity, and adding detail to >>>> theories before simpler explanations have failed is not Ockham's >>>> heuristic. That said it's still in the space of a Big Data fuzzy >>>> science approach, where we throw as much data from as many levels of >>>> analysis as we can come up with into a big pot and then construct a >>>> theory. The thing to keep in mind is that when we start pruning this >>>> model most of the details are going to disappear, because almost all >>>> of them are irrelevant. Indeed, the size of the description that >>>> includes all the details is almost infinite, whereas the length of >>>> the description that explains almost all the variance is extremely >>>> short, especially in comparison. This is why Ockham's razor is a good >>>> heuristic. It helps prevent us from wasting time on unnecessary >>>> details by suggesting that we only inquire as to the details once our >> existing simpler theory has failed to work. >>>> >>>> On 3/14/14 3:40 PM, Michael Arbib wrote: >>>> >>>> At 11:17 AM 3/14/2014, Juyang Weng wrote: >>>> >>>> The brain uses a single architecture to do all brain functions we are >>>> aware of! It uses the same architecture to do vision, audition, >>>> motor, reasoning, decision making, motivation (including pain >>>> avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). >>>> >>>> >>>> Gosh -- and I thought cerebral cortex, hippocampus and cerebellum >>>> were very different from each other. >>>> >>>> >>>> >> >> Troy D. Kelley >> RDRL-HRS-E >> Cognitive Robotics and Modeling Team Leader Human Research and Engineering >> Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone >> 410-278-5869 or 410-278-6748 Note my new email address: >> troy.d.kelley6.civ at mail.mil >> >> >> >> Classification: UNCLASSIFIED >> Caveats: NONE >> >> Stephen Grossberg Wang Professor of Cognitive and Neural Systems Professor of Mathematics, Psychology, and Biomedical Engineering Director, Center for Adaptive Systems http://www.cns.bu.edu/about/cas.html http://cns.bu.edu/~steve steve at bu.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From grlmc at urv.cat Sat Mar 22 12:41:24 2014 From: grlmc at urv.cat (GRLMC) Date: Sat, 22 Mar 2014 17:41:24 +0100 Subject: Connectionists: SLSP 2014: 2nd call for papers Message-ID: <4BFE0ADE9BA94FF9918963122ED7BE16@Carlos1> *To be removed from our mailing list, please respond to this message with UNSUBSCRIBE in the subject line* **************************************************************************** ****** 2nd INTERNATIONAL CONFERENCE ON STATISTICAL LANGUAGE AND SPEECH PROCESSING SLSP 2014 Grenoble, France October 14-16, 2014 Organised by: ?quipe GETALP Laboratoire d?Informatique de Grenoble Research Group on Mathematical Linguistics (GRLMC) Rovira i Virgili University http://grammars.grlmc.com/slsp2014/ **************************************************************************** ****** AIMS: SLSP is a yearly conference series aimed at promoting and displaying excellent research on the wide spectrum of statistical methods that are currently in use in computational language or speech processing. It aims at attracting contributions from both fields. Though there exist large, well-known conferences and workshops hosting contributions to any of these areas, SLSP is a more focused meeting where synergies between subdomains and people will hopefully happen. In SLSP 2014, significant room will be reserved to young scholars at the beginning of their career and particular focus will be put on methodology. VENUE: SLSP 2014 will take place in Grenoble, at the foot of the French Alps. SCOPE: The conference invites submissions discussing the employment of statistical methods (including machine learning) within language and speech processing. The list below is indicative and not exhaustive: phonology, phonetics, prosody, morphology syntax, semantics discourse, dialogue, pragmatics statistical models for natural language processing supervised, unsupervised and semi-supervised machine learning methods applied to natural language, including speech statistical methods, including biologically-inspired methods similarity alignment language resources part-of-speech tagging parsing semantic role labelling natural language generation anaphora and coreference resolution speech recognition speaker identification/verification speech transcription speech synthesis machine translation translation technology text summarisation information retrieval text categorisation information extraction term extraction spelling correction text and web mining opinion mining and sentiment analysis spoken dialogue systems author identification, plagiarism and spam filtering STRUCTURE: SLSP 2014 will consist of: invited talks invited tutorials peer-reviewed contributions INVITED SPEAKERS: Claire Gardent (LORIA, Nancy, FR), Grammar Based Sentence Generation and Statistical Error Mining Roger K. Moore (Sheffield, UK), Spoken Language Processing: Time to Look Outside? Martti Vainio (Helsinki, FI), Phonetics and Machine Learning: Hierarchical Modelling of Prosody in Statistical Speech Synthesis PROGRAMME COMMITTEE: Sophia Ananiadou (Manchester, UK) Srinivas Bangalore (Florham Park, US) Patrick Blackburn (Roskilde, DK) Herv? Bourlard (Martigny, CH) Bill Byrne (Cambridge, UK) Nick Campbell (Dublin, IE) David Chiang (Marina del Rey, US) Kenneth W. Church (Yorktown Heights, US) Walter Daelemans (Antwerpen, BE) Thierry Dutoit (Mons, BE) Alexander Gelbukh (Mexico City, MX) James Glass (Cambridge, US) Ralph Grishman (New York, US) Sanda Harabagiu (Dallas, US) Xiaodong He (Redmond, US) Hynek Hermansky (Baltimore, US) Hitoshi Isahara (Toyohashi, JP) Lori Lamel (Orsay, FR) Gary Geunbae Lee (Pohang, KR) Haizhou Li (Singapore, SG) Daniel Marcu (Los Angeles, US) Carlos Mart?n-Vide (Tarragona, ES, chair) Manuel Montes-y-G?mez (Puebla, MX) Satoshi Nakamura (Nara, JP) Shrikanth S. Narayanan (Los Angeles, US) Vincent Ng (Dallas, US) Joakim Nivre (Uppsala, SE) Elmar N?th (Erlangen, DE) Maurizio Omologo (Trento, IT) Mari Ostendorf (Seattle, US) Barbara H. Partee (Amherst, US) Gerald Penn (Toronto, CA) Massimo Poesio (Colchester, UK) James Pustejovsky (Waltham, US) Ga?l Richard (Paris, FR) German Rigau (San Sebasti?n, ES) Paolo Rosso (Valencia, ES) Yoshinori Sagisaka (Tokyo, JP) Bj?rn W. Schuller (London, UK) Satoshi Sekine (New York, US) Richard Sproat (New York, US) Mark Steedman (Edinburgh, UK) Jian Su (Singapore, SG) Marc Swerts (Tilburg, NL) Jun'ichi Tsujii (Beijing, CN) Gertjan van Noord (Groningen, NL) Renata Vieira (Porto Alegre, BR) Dekai Wu (Hong Kong, HK) Feiyu Xu (Berlin, DE) Roman Yangarber (Helsinki, FI) Geoffrey Zweig (Redmond, US) ORGANISING COMMITTEE: Laurent Besacier (Grenoble, co-chair) Adrian Horia Dediu (Tarragona) Benjamin Lecouteux (Grenoble) Carlos Mart?n-Vide (Tarragona, co-chair) Florentina Lilica Voicu (Tarragona) SUBMISSIONS: Authors are invited to submit non-anonymized papers in English presenting original and unpublished research. Papers should not exceed 12 single-spaced pages (including eventual appendices) and should be prepared according to the standard format for Springer Verlag's LNAI/LNCS series (see http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). Submissions have to be uploaded to: https://www.easychair.org/conferences/?conf=slsp2014 PUBLICATIONS: A volume of proceedings published by Springer in the LNAI/LNCS series will be available by the time of the conference. A special issue of a major journal will be later published containing peer-reviewed extended versions of some of the papers contributed to the conference. Submissions to it will be by invitation. REGISTRATION: The period for registration is open from January 16, 2014 to October 14, 2014. The registration form can be found at: http://grammars.grlmc.com/slsp2014/Registration.php DEADLINES: Paper submission: May 7, 2014 (23:59h, CET) Notification of paper acceptance or rejection: June 18, 2014 Final version of the paper for the LNAI/LNCS proceedings: June 25, 2014 Early registration: July 2, 2014 Late registration: September 30, 2014 Submission to the post-conference journal special issue: January 16, 2015 QUESTIONS AND FURTHER INFORMATION: florentinalilica.voicu at urv.cat POSTAL ADDRESS: SLSP 2014 Research Group on Mathematical Linguistics (GRLMC) Rovira i Virgili University Av. Catalunya, 35 43002 Tarragona, Spain Phone: +34 977 559 543 Fax: +34 977 558 386 ACKNOWLEDGEMENTS: Departament d?Economia i Coneixement, Generalitat de Catalunya Laboratoire d?Informatique de Grenoble Universitat Rovira i Virgili From michel.verleysen at uclouvain.be Sat Mar 22 11:07:09 2014 From: michel.verleysen at uclouvain.be (Michel Verleysen) Date: Sat, 22 Mar 2014 15:07:09 +0000 Subject: Connectionists: ESANN 2014: program Message-ID: <426e1f454b604959be86269cd47a98d0@ucl-mbx04.OASIS.UCLOUVAIN.BE> We apologize for possible duplicates of this message sent to distribution lists. ESANN 2014: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges, Belgium, 23-24-25 April 2014 http://www.esann.org/ Preliminary program The preliminary program of the ESANN 2014 conference is now available on the Web: http://www.esann.org/ For those of you who maintain WWW pages including lists of related machine learning and artificial neural networks sites: we would appreciate if you could add the above URL to your list; thank you very much! For 22 years the ESANN conference has become a major event in the field of neural computation and machine learning. ESANN is a selective conference focusing on fundamental aspects of artificial neural networks, machine learning, statistical information processing and computational intelligence. Mathematical foundations, algorithms and tools, and applications are covered. The titles of the sessions are: - Advances in Spiking Neural Information Processing Systems (SNIPS) - Vector quantization- and nearest neighbor-based methods - Byte the bullet: learning on real-world computing architectures - Reinforcement learning and optimization - Nonlinear dimensionality reduction - Signal and temporal processing - Learning of structured and non-standard data - Kernel methods - Learning and Modeling Big Data - Classification - Dynamical systems and online learning - Advances on Weightless Neural Systems - Clustering - Regression, Forecasting and Extreme Learning Machines - Label noise in classification The program of the conference can be found at http://www.esann.org/, together with practical information about the conference venue, registration, etc. Other information can be obtained by sending an e-mail to esann at uclouvain.be. Venue ------ The conference will be held in Bruges (also called "Venice of the North"), one of the most beautiful medieval towns in Europe. Bruges can be reached by train from Brussels in less than one hour (frequent trains). Designated as the "Venice of the North", the city has preserved all the charms of the medieval heritage. Its centre, which is inscribed on the Unesco World Heritage list, is in itself a real open air museum. Steering and local committee ---------------------------- Fran?ois Blayo Ipseite (CH) Gianluca Bontempi Univ. Libre Bruxelles (B) Marie Cottrell Univ. Paris 1 Panth?on-Sorbonne (F) Mia Loccufier Univ. Gent (B) Bernard Manderick Vrije Univ. Brussel (B) Jean-Pierre Peters FUNDP Namur (B) Johan Suykens K. U. Leuven (B) Joos Vandewalle K.U. Leuven (B) Michel Verleysen UCL Louvain-la-Neuve Louis Wehenkel Univ. Li?ge (B) Scientific committee -------------------- Fabio Aiolli Univ. degli Studi di Padova (I) Davide Anguita Univ. of Genoa (I) Cecilio Angulo Univ. Polit. de Catalunya (E) Aluizio Ara?jo Univ. Federal de Pernambuco (Brazil) C?dric Archambeau Amazon.com (D) Miguel Atencia Univ. Malaga (E) Michael Aupetit CEA (F) Andreas Backhaus Fraunhofer-Institut (D) Llu?s Belanche Univ. Polit. de Catalunya (E) Youn?s Bennani Universit? Paris 13 (F) Michael Biehl Univ. of Groningen (NL) Martin Bogdan Univ. Leipzig (D) Herv? Bourlard IDIAP Martigny (CH) Charles Bouveyron Univ. Paris Descartes (F) Antonio Braga Federal Univ. of Minas Gerais (Brazil) Joan Cabestany Univ. Polit. de Catalunya (E) St?phane Canu Inst. Nat. Sciences App. (F) Andreu Catal? Univ. Polit. de Catalunya (E) Sylvain Chevallier University of Versailles (F) Valentina Colla Scuola Sup. Sant'Anna Pisa (I) Nigel Crook Oxford Brookes University (UK) Holk Cruse Universit?t Bielefeld (D) Giovanni Da San Martino Univ. of Padova (I) Bernard de Baets Univ. Gent (B) Kris De Brabanter K. U. Leuven (B) Massimo De Gregorio Istituto di Cibernetica-CNR (I) Dante Del Corso Politecnico di Torino (I) Wlodek Duch Nicholas Copernicus Univ. (PL) Marc Duranton CEA Saclay (F) Richard Duro Univ. Coruna (E) Mark Embrechts Rensselaer Polytechnic Institute (USA) Anibal Figueiras-Vidal Univ. Carlos III Madrid (E) Jean-Claude Fort Univ. Paris Descartes (F) Felipe M. G. Fran?a Univ. Federal Rio de Janeiro (Brazil) Leonardo Franco Univ. Malaga (E) Damien Fran?ois Universit? catholique de Louvain (B) Manjunath Gandhi Jacobs University (D) Alessandro Ghio Univ. of Genova (I) Luis Gonzalez Abril University of Sevilla (E) Marco Gori Univ. Siena (I) Bernard Gosselin Univ. Mons (B) Philipe-Henri Gosselin INRIA Rennes Bretagne Atl. (F) Manuel Grana UPV San Sebastian (E) Anne Gu?rin-Dugu? IMAG Grenoble (F) Barbara Hammer Bielefeld Univ. (D) Verena Heidrich-Meisner CAU Kiel (D) Tom Heskes Univ. Nijmegen (NL) Katerina Hlavackova-Schindler Univ. Life Sci. & Natural Resources (A) Christian Igel Univ. Copenhagen (DK) Jose Jerez Univ. Malaga (E) Gonzalo Joya Univ. Malaga (E) Christian Jutten INPG Grenoble (F) Juha Karhunen Aalto Univ. (FIN) Marika K?stner Univ. Apllied Sciences Mittweida (D) DaeEun Kim Yonsei Univ. (South Korea) Stefanos Kollias National Tech. Univ. Athens (GR) Jouko Lampinen Aalto Univ. (FIN) Petr Lansky Acad. of Sci. of the Czech Rep. (CZ) Beatrice Lazzerini Univ. Pisa (I) John Lee Univ. cat Louvain (B) Minho Lee Kyungpook National Univ. (KR) Soo-Young Lee Korea Adv. Inst. Sci. & Tech. (KR) Amaury Lendasse Aalto Univ. (FIN) Priscila M. V. Lima Univ. Fed. Rural Rio de Janeiro (Brazil) Paulo Lisboa Liverpool John Moores Univ. (UK) Jos? D. Mart?n Univ. of Valencia (E) Erzsebet Merenyi Rice Univ. (USA) David Meunier Univ. Claude Bernard Lyon 1 (F) Anke Meyer-B?se Florida State Univ. (USA) Yoan Miche Aalto Univ. (FIN) Alessio Micheli Univ. of Pisa (I) Erkki Oja Aalto Univ. (FIN) Tjeerd olde Scheper Oxford Brookes University (UK) Madalina Olteanu Univ. Paris 1 Panth?on-Sorbonne (F) Gilles Pag?s Univ. Pierre et Marie Curie (Paris 6) (F) Kristiaan Pelckmans Uppsala University (SE) Jaakko Peltonon Aalto University (FIN) David Picard Universit? de Cergy-Pontoise (F) Gadi Pinkas The Center for Acad. Studies (Israel) Alberto Prieto Universitad de Granada (E) Didier Puzenat Univ. Antilles-Guyane (F) Jean-Pierre Rospars INRA Versailles (F) Fabrice Rossi Univ. Paris 1 Panth?on-Sorbonne (F) Ulrich R?ckert Bielefeld University (D) David Saad Aston Univ. (UK) Francisco Sandoval Univ.Malaga (E) Jose Santos Reyes Univ. Coruna (E) Frank-Michael Schleif Univ. Bielefeld (D) Udo Seiffert Fraunhofer-Institute IFF Magdeburg (D) Alessandro Sperduti Universit? degli Studi di Padova (I) Jochen Steil Univ. Bielefeld (D) Peter Tino Univ. of Birmingham (UK) Claude Touzet Univ. Provence (F) Vanya Van Belle KUL Leuven (B) Marc Van Hulle K. U. Leuven (B) Alfredo Vellido Polytechnic University of Catalonia (E) Thomas Villmann Univ. Apllied Sciences Mittweida (D) Willem Waegeman Univ. Gent (B) Heiko Wersing Honda Research Institute Europe (D) Axel Wism?ller Univ. of Rochester, New York (USA) ======================================================== ESANN - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning http://www.esann.org/ * For submissions of papers, reviews, registrations: Michel Verleysen Univ. Cath. de Louvain - Machine Learning Group 3, pl. du Levant - B-1348 Louvain-la-Neuve - Belgium tel: +32 10 47 25 51 - fax: + 32 10 47 25 98 mailto:esann at uclouvain.be * Conference secretariat d-side conference services 24 av. L. Mommaerts - B-1140 Evere - Belgium tel: + 32 2 730 06 11 - fax: + 32 2 730 06 00 mailto:esann at uclouvain.be ======================================================= -------------- next part -------------- An HTML attachment was scrubbed... URL: From nicosia at dmi.unict.it Sat Mar 22 12:24:10 2014 From: nicosia at dmi.unict.it (Giuseppe Nicosia) Date: Sat, 22 Mar 2014 17:24:10 +0100 Subject: Connectionists: SSBSS 2014 News & 2nd CfP: Int. Synthetic & Systems Biology Summer School: Biology meets Engineering & Computer Science, Taormina - Sicily, Italy, June 15-19, 2014 Message-ID: <613A7598-D4E5-46DA-BA82-53EBE2316536@dmi.unict.it> 2nd Call for Participation (apologies for multiple copies) ______________________________________________________ Synthetic and Systems Biology Summer School: Biology meets Engineering and Computer Science, Taormina - Sicily, Italy, June 15-19, 2014 http://www.taosciences.it/ssbss2014/ ssbss2014 at dmi.unict.it We are pleased to inform that we received more than 120 applications and 60 Abstracts/Posters and, due to many requests, we are extending the application *deadline to March 31, 2014.* For this reason, we will have up to ~150 slots (no 100 slots as previously written) for selected and motivated students. *Application Deadline: March 31, 2014* * Speakers & Courses * + Uri Alon, Weizmann Institute of Science, Israel Lecture I: Elementary Circuits in Biology Lecture II: Evolution and Optimality of Gene Circuits + Joel Bader, Johns Hopkins University, USA Lecture I: Network Remodeling during Development and Disease Lecture II: Gene and Pathway Analysis of Genome-wide Association Studies + Jef Boeke, Johns Hopkins University, USA Lecture I: Genome Synthesis Lecture II: Combinatorial DNA Assembly methods and their applications + Jason Chin, MRC Laboratory of Molecular Biology, UK Lecture I: Reprogramming the Genetic Code + Virginia Cornish, Columbia University, USA Lecture : TBA + Paul Freemont, Imperial College London, UK Lecture I: Foundational Technologies for Synthetic Biology - from DNA Assembly to Part Characterisation Lecture II: Synthetic biology designs for biosensor applications + Farren Isaacs, Yale University, USA Lecture I: Genome engineering technologies for rapid editing & evolution organisms Lecture II: Design, construction & function of genomically recoded organisms + Tanja Kortemme, University of California San Francisco, USA Lecture I: Computational protein design - principles, challenges and progress Lecture II: Design of reprogrammed and new functions - from proteins to cells + Giuseppe Nicosia, University of Catania, Italy Lecture I: Biological Circuit Design by Pareto Optimality Lecture II: Programming Living Molecular Machines for Biofuel Production + Sven Panke, ETH, Switzerland Lecture I: Synthetic Biology of Cell free Systems Lecture II: Exploiting Engineered Cell-Cell Communications in Large Scale Biotechnology + Rahul Sarpeshkar, MIT, USA Lecture I: Analog versus Digital Computation in Biology Lecture II: Analog Synthetic and Systems Biology + Giovanni Stracquadanio, Johns Hopkins University, USA Lecture I: Minimal Genomes: High-Throughput Sequencing, Statistical Methods and Physics Models to Unveil Minimal Yeast Chromosomes Compatible with Life Lecture II: Computational Tools for Genome editing, Combinatorial Assembly and Workflow Tracking + Ron Weiss, MIT, USA Lecture : TBA + Workshop on "Biosensors and synthetic circuits in mammalian cells" *School Directors* + Jef Boeke, Johns Hopkins University, USA + Giuseppe Nicosia, University of Catania, Italy + Mario Pavone, University of Catania, Italy + Giovanni Stracquadanio, Johns Hopkins University, USA *Short Talk and Poster Submission* Students may submit a research abstract for presentation. School directors will review the abstracts and will recommend for poster or short-oral presentation. Abstract should be submitted by *February 15, 2014*. The abstracts will be published on the electronic hands-out material of the summer school. Co-located Event: The 3rd International Synthetic Yeast Genome (Sc2.0) Meeting will be held in Taormina Friday June 20, 2014 http://www.taosciences.it/ssbss2014/ ssbss2014 at dmi.unict.it -- Giuseppe Nicosia, Ph.D. Associate Professor Dept of Mathematics & Computer Science University of Catania Viale A. Doria, 6 - 95125 Catania, Italy P +39 095 7383048 E nicosia at dmi.unict.it W http://www.dmi.unict.it/nicosia ----------------------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From achler at gmail.com Sat Mar 22 16:38:28 2014 From: achler at gmail.com (Tsvi Achler) Date: Sat, 22 Mar 2014 13:38:28 -0700 Subject: Connectionists: how the brain works? (UNCLASSIFIED) In-Reply-To: References: <51FDF3A2-717D-4399-9076-331EEA0FC45A@lehigh.edu> Message-ID: Dear Steve, Isn't Section 8.2 exactly about the cycling (and labeled as such) and figure 2 a depiction of the cycling? Your response is similar to feedback I received years ago in an evaluation of my algorithm, where the reviewer clearly didnt read the paper in detail but it seems was intent not to let it through because it seemed as if they had their own algorithm and agenda. I took out snippets of that review and placed it here because this is continuing today. >From the review: "The network, in effect, implements a winner-takes all scheme, when only a single output neuron reacts to each input..." This is not true my network does not implement a Winner take all, in fact that is the point, there is no lateral inhibition. "... this network type can be traced back to the sixties, most notably to the work of Grossberg ... As a result, I do not see any novelty in this paper ... Overall recommendation: 1 (Reject) .. Reviewer's confidence: 5 (highest)." This is exactly what I mean when I stated that it seems academia would rather bury new ideas. Such a callous and strong dismissal is devastating to a young student and detrimental to the field. Someone as decorated and established as you has the opportunity to move the field forward. However instead the neural network aspect of feedback during recognition is being actively inhibited by unsubstantive and destructive efforts. I would be happy to work with you offline to write a joint statement on this with all of the technical details. Sincerely, -Tsvi On Mar 22, 2014 4:08 AM, "Stephen Grossberg" wrote: > > Dear Tsvi, > > You mention Adaptive Resonance below and suggest that it "requires complex signals indicating when to stop, compare, and cycle". That is not correct. > > ART uses a simple measure of pattern mismatch. Moreover, psychological, neurophysiological, anatomical, and ERP data support the operations that it models during hypothesis testing and memory search. ART predicted various of these data before they were collected. > > If you would like to pursue this further, see http://cns.bu.edu/~steve/ART.pdf for a recent heuristic review. > > Best, > > Steve > > > On Mar 21, 2014, at 10:29 PM, Tsvi Achler wrote: > > Sorry for the length of this response but I wanted to go into some > detail here. > > I see the habituation paradigm as somewhat analogous to surprise and > measurement of error during recognition. I can think of a few > mathematical Neural Network classifiers that can generate an internal > pattern for match during recognition to calculate this > habituation/surprise orientation. Feedforward networks definitely > will not work because they don't recall the internal stimulus very > well. One option is adaptive resonance (which I assume you use), but > it cycles through the patterns one at a time and requires complex > signals indicating when to stop, compare, and cycle. I assume > Juyang's DN can also do something similar but I suspect it also must > cycle since it also has lateral inhibition. Bidirectional Associative > Memories (BAM) may also be used. Others such as Bayes networks and > free-energy principle can used, although they are not as easily > translatable to neural networks. > > Another option is a network like mine which does not have lateral > connections but also generates internal patterns. The advantage is > that it can also generate mixtures of patterns at once, does not cycle > through individual patterns, does not require signals associated with > cycling, and can be shown mathematically to be analogous to > feedforward networks. The error signal it produces can be used for an > orientation reflex or what I rather call attention. It is essential > for recognition and planning. > > I would be happy to give a talk on this and collaborate on a rigorous > comparison. Indeed it is important to look at models other than those > using feedforward connections during recognition. > > Sincerely, > > -Tsvi > > > > > On Mar 21, 2014 5:25 AM, "Kelley, Troy D CIV (US)" > wrote: > > > Classification: UNCLASSIFIED > > Caveats: NONE > > > Yes, Mark, I would argue that habituation is anticipatory prediction. The > > neuron creates a model of the incoming stimulus and the neuron is > > essentially predicting that the next stimuli will be comparatively similar > > to the previous stimulus. If this prediction is met, the neuron habituates. > > That is a simple, low level, predictive model. > > > -----Original Message----- > > From: Mark H. Bickhard [mailto:mhb0 at Lehigh.EDU] > > Sent: Thursday, March 20, 2014 5:28 PM > > To: Kelley, Troy D CIV (US) > > Cc: Tsvi Achler; Andras Lorincz; bower at uthscsa.edu; > > connectionists at mailman.srv.cs.cmu.edu > > Subject: Re: Connectionists: how the brain works? > > > I would agree with the importance of Sokolov habituation, but there is more > > than one way to understand and generalize from this phenomenon: > > > http://www.lehigh.edu/~mhb0/AnticipatoryBrain20Aug13.pdf > > > Mark H. Bickhard > > Lehigh University > > 17 Memorial Drive East > > Bethlehem, PA 18015 > > mark at bickhard.name > > http://bickhard.ws/ > > > On Mar 20, 2014, at 4:41 PM, Kelley, Troy D CIV (US) wrote: > > > We have found that the habituation algorithm that Sokolov discovered way > > back in 1963 provides an useful place to start if one is trying to determine > > how the brain works. The algorithm, at the cellular level, is capable of > > determining novelty and generating implicit predictions - which it then > > habituates to. Additionally, it is capable of regenerating the original > > response when re-exposed to the same stimuli. All of these behaviors > > provide an excellent framework at the cellular level for explain all sorts > > of high level behaviors at the functional level. And it fits the Ockham's > > razor principle of using a single algorithm to explain a wide variety of > > explicit behavior. > > > Troy D. Kelley > > RDRL-HRS-E > > Cognitive Robotics and Modeling Team Leader Human Research and Engineering > > Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone > > 410-278-5869 or 410-278-6748 Note my new email address: > > troy.d.kelley6.civ at mail.mil > > > > > > > On 3/20/14 10:41 AM, "Tsvi Achler" wrote: > > > I think an Ockham's razor principle can be used to find the most > > optimal algorithm if it is interpreted to mean the model with the > > least amount of free parameters that captures the most phenomena. > > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.p > > df > > -Tsvi > > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz > > wrote: > > Ockham works here via compressing both the algorithm and the structure. > > Compressing the structure to stem cells means that the algorithm > > should describe the development, the working, and the time dependent > > structure of the brain. Not compressing the description of the > > structure of the evolved brain is a different problem since it saves > > the need for the description of the development, but the working. > > Understanding the structure and the working of one part of the brain > > requires the description of its communication that increases the > > complexity of the description. By the way, this holds for the whole > > brain, so we might have to include the body at least; a structural > > minimist may wish to start from the genetic code, use that hint and > > unfold the already compressed description. There are (many and > > different) todos 'outside' ... > > > > Andras > > > > > > . > > > ________________________________ > > From: Connectionists > > on behalf of james bower > > Sent: Thursday, March 20, 2014 3:33 AM > > > To: Geoffrey Goodhill > > Cc: connectionists at mailman.srv.cs.cmu.edu > > Subject: Re: Connectionists: how the brain works? > > > Geoffrey, > > > Nice addition to the discussion actually introducing an interesting > > angle on the question of brain organization (see below) As you note, > > reaction diffusion mechanisms and modeling have been quite successful > > in replicating patterns seen in biology - especially interesting I > > think is the modeling of patterns in slime molds, but also for very > > general pattern formation in embryology. However, more and more > > detailed analysis of what is diffusing, what is sensing what is > > diffusing, and what is reacting to substances once sensed -- all > > linked to complex patterns of gene regulation and expression have > > made it clear that actual embryological development is much much more > > complex, as Turing himself clearly anticipated, as the quote you cite pretty > > clearly indicates. Clearly a smart guy. But, I don't actually think > > that > > this is an application of Ochham's razor although it might appear to > > be after the fact. Just as Hodgkin and Huxley were not applying it > > either in > > their model of the action potential. Turing apparently guessed (based > > on a > > lot of work at the time on pattern formation with reaction diffusion) > > that such a mechanism might provide the natural basis for what > > embryos do. Thus, just like for Hodgkin and Huxley, his model > > resulted from a bio-physical insight, not an explicit attempt to > > build a stripped down model for its own sake. I seriously doubt > > that Turning would have claimed that he, or his models could more > > effectively do what biology actually does in forming an embrio, or > > substitute for the actual process. > > > However, I think there is another interesting connection here to the > > discussion on modeling the brain. Almost certainly communication and > > organizational systems in early living beings were reaction diffusion > > based. > > This is still a dominant effect for many 'sensing' in small organisms. > > Perhaps, therefore, one can look at nervous systems as structures > > specifically developed to supersede reaction diffusion mechanisms, > > thus superseding this very 'natural' but complexity limited type of > > communication and organization. What this means, I believe, is that > > a simplified or abstracted physical or mathematical model of the > > brain explicitly violates the evolutionary pressures responsible for > > its structure. Its where the wires go, what the wires do, and what > > the receiving neuron does with the information that forms the basis > > for neural computation, multiplied by a very large number. And that > > is dependent on the actual physical structure of those elements. > > > One more point about smart guys, as a young computational > > neurobiologist I questioned how insightful John von Neumann actually > > was because I was constantly hearing about a lecture he wrote (but > > didn't give) at Yale suggesting that dendrites and neurons might be > > digital ( John von Neumann's The Computer and the Brain. (New > > Haven/London: Yale Univesity Press, 1958.) Very clearly a not very > > insightful idea for a supposedly smart guy. It wasn't until a few > > years later, when I actually read the lecture - that I found out that > > he ends by stating that this idea is almost certainly wrong, given > > the likely nonlinearities in neuronal dendrites. So von Neumann > > didn't lack insight, the people who quoted him did. It is a > > remarkable fact that more than 60 years later, the majority of models of > > so called neurons built by engineers AND neurobiologists don't consider > > these nonlinearities. > > The point being the same point, to the Hopfield, Mead, Feynman list, > > we can now add Turing and von Neumann as suspecting that for > > understanding, biology and the nervous system must be dealt with in their > > full complexity. > > > But thanks for the example from Turing - always nice to consider actual > > examples. :-) > > > Jim > > > > > > > On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill > > wrote: > > > Hi All, > > > A great example of successful Ockham-inspired biology is Alan > > Turing's model for pattern formation (spots, stripes etc) in > > embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, > > 1953). Turing introduced a physical mechanism for how inhomogeneous > > spatial patterns can arise in a biological system from a spatially > > homogeneous starting point, based on the diffusion of morphogens. The > > paper begins: > > > "In this section a mathematical model of the growing embryo will be > > described. This model will be a simplification and an idealization, > > and consequently a falsification. It is to be hoped that the features > > retained for discussion are those of greatest importance in the > > present state of knowledge." > > > The paper remained virtually uncited for its first 20 years following > > publication, but since then has amassed 8000 citations (Google > > Scholar). The subsequent discovery of huge quantities of molecular > > detail in biological pattern formation have only reinforced the > > importance of this relatively simple model, not because it explains > > every system, but because the overarching concepts it introduced have > > proved to be so fertile. > > > Cheers, > > > Geoff > > > > On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: > > > Ignoring the gross differences in circuitry between hippocampus and > > cerebellum, etc., is not erring on the side of simplicity, it is > > erring, period. Have you actually looked at a > > Cajal/Sxentagothai-style drawing of their circuitry? > > > At 01:07 PM 3/19/2014, Brian J Mingus wrote: > > > Hi Jim, > > > Focusing too much on the details is risky in and of itself. Optimal > > compression requires a balance, and we can't compute what that > > balance is (all models are wrong). One thing we can say for sure is > > that we should err on the side of simplicity, and adding detail to > > theories before simpler explanations have failed is not Ockham's > > heuristic. That said it's still in the space of a Big Data fuzzy > > science approach, where we throw as much data from as many levels of > > analysis as we can come up with into a big pot and then construct a > > theory. The thing to keep in mind is that when we start pruning this > > model most of the details are going to disappear, because almost all > > of them are irrelevant. Indeed, the size of the description that > > includes all the details is almost infinite, whereas the length of > > the description that explains almost all the variance is extremely > > short, especially in comparison. This is why Ockham's razor is a good > > heuristic. It helps prevent us from wasting time on unnecessary > > details by suggesting that we only inquire as to the details once our > > existing simpler theory has failed to work. > > > On 3/14/14 3:40 PM, Michael Arbib wrote: > > > At 11:17 AM 3/14/2014, Juyang Weng wrote: > > > The brain uses a single architecture to do all brain functions we are > > aware of! It uses the same architecture to do vision, audition, > > motor, reasoning, decision making, motivation (including pain > > avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). > > > > Gosh -- and I thought cerebral cortex, hippocampus and cerebellum > > were very different from each other. > > > > > > Troy D. Kelley > > RDRL-HRS-E > > Cognitive Robotics and Modeling Team Leader Human Research and Engineering > > Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone > > 410-278-5869 or 410-278-6748 Note my new email address: > > troy.d.kelley6.civ at mail.mil > > > > > Classification: UNCLASSIFIED > > Caveats: NONE > > > > > Stephen Grossberg > Wang Professor of Cognitive and Neural Systems > Professor of Mathematics, Psychology, and Biomedical Engineering > Director, Center for Adaptive Systems http://www.cns.bu.edu/about/cas.html > http://cns.bu.edu/~steve > steve at bu.edu > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From steve at cns.bu.edu Sat Mar 22 22:04:46 2014 From: steve at cns.bu.edu (Stephen Grossberg) Date: Sat, 22 Mar 2014 22:04:46 -0400 Subject: Connectionists: how the brain works? (UNCLASSIFIED) In-Reply-To: References: <51FDF3A2-717D-4399-9076-331EEA0FC45A@lehigh.edu> Message-ID: Dear Tsvi, You stated that ART "requires complex signals". I noted that this statement is not correct. To illustrate what I meant, I noted that ART uses a simple measure of pattern mismatch. In particular, a top-down expectation selects consistent features and suppresses inconsistent features to focus attention upon expected features. This property of attention is well supported by lots of psychological and neurobiological data. You also contrasted models that "cycle", one being ART, with your own network which "does not cycle". I therefore mentioned that ART hypothesis testing and search, which involve "cycling" through operations of mismatch, arousal, and reset, are directly supported by a lot of data. For example, in an oddball paradigm, one can compare mismatch with properties of the P120 ERP, arousal with properties of the N200 ERP, and reset with properties of the P300 ERP. I am not sure why this reply led you to write bitterly about an old review of one of your articles, which I know nothing about, and which is not relevant to my specific points of information. Best, Steve On Mar 22, 2014, at 4:38 PM, Tsvi Achler wrote: > Dear Steve, > Isn't Section 8.2 exactly about the cycling (and labeled as such) and figure 2 a depiction of the cycling? > > Your response is similar to feedback I received years ago in an evaluation of my algorithm, where the reviewer clearly didnt read the paper in detail but it seems was intent not to let it through because it seemed as if they had their own algorithm and agenda. I took out snippets of that review and placed it here because this is continuing today. > > From the review: "The network, in effect, implements a winner-takes all scheme, when only a single output neuron reacts to each input..." > This is not true my network does not implement a Winner take all, in fact that is the point, there is no lateral inhibition. > "... this network type can be traced back to the sixties, most notably to the work of Grossberg ... As a result, I do not see any novelty in this paper ... Overall recommendation: 1 (Reject) .. Reviewer's confidence: 5 (highest)." > > This is exactly what I mean when I stated that it seems academia would rather bury new ideas. Such a callous and strong dismissal is devastating to a young student and detrimental to the field. > Someone as decorated and established as you has the opportunity to move the field forward. However instead the neural network aspect of feedback during recognition is being actively inhibited by unsubstantive and destructive efforts. > > I would be happy to work with you offline to write a joint statement on this with all of the technical details. > > Sincerely, > -Tsvi > > > On Mar 22, 2014 4:08 AM, "Stephen Grossberg" wrote: > > > > Dear Tsvi, > > > > You mention Adaptive Resonance below and suggest that it "requires complex signals indicating when to stop, compare, and cycle". That is not correct. > > > > ART uses a simple measure of pattern mismatch. Moreover, psychological, neurophysiological, anatomical, and ERP data support the operations that it models during hypothesis testing and memory search. ART predicted various of these data before they were collected. > > > > If you would like to pursue this further, see http://cns.bu.edu/~steve/ART.pdf for a recent heuristic review. > > > > Best, > > > > Steve > > > > > > On Mar 21, 2014, at 10:29 PM, Tsvi Achler wrote: > > > > Sorry for the length of this response but I wanted to go into some > > detail here. > > > > I see the habituation paradigm as somewhat analogous to surprise and > > measurement of error during recognition. I can think of a few > > mathematical Neural Network classifiers that can generate an internal > > pattern for match during recognition to calculate this > > habituation/surprise orientation. Feedforward networks definitely > > will not work because they don't recall the internal stimulus very > > well. One option is adaptive resonance (which I assume you use), but > > it cycles through the patterns one at a time and requires complex > > signals indicating when to stop, compare, and cycle. I assume > > Juyang's DN can also do something similar but I suspect it also must > > cycle since it also has lateral inhibition. Bidirectional Associative > > Memories (BAM) may also be used. Others such as Bayes networks and > > free-energy principle can used, although they are not as easily > > translatable to neural networks. > > > > Another option is a network like mine which does not have lateral > > connections but also generates internal patterns. The advantage is > > that it can also generate mixtures of patterns at once, does not cycle > > through individual patterns, does not require signals associated with > > cycling, and can be shown mathematically to be analogous to > > feedforward networks. The error signal it produces can be used for an > > orientation reflex or what I rather call attention. It is essential > > for recognition and planning. > > > > I would be happy to give a talk on this and collaborate on a rigorous > > comparison. Indeed it is important to look at models other than those > > using feedforward connections during recognition. > > > > Sincerely, > > > > -Tsvi > > > > > > > > > > On Mar 21, 2014 5:25 AM, "Kelley, Troy D CIV (US)" > > wrote: > > > > > > Classification: UNCLASSIFIED > > > > Caveats: NONE > > > > > > Yes, Mark, I would argue that habituation is anticipatory prediction. The > > > > neuron creates a model of the incoming stimulus and the neuron is > > > > essentially predicting that the next stimuli will be comparatively similar > > > > to the previous stimulus. If this prediction is met, the neuron habituates. > > > > That is a simple, low level, predictive model. > > > > > > -----Original Message----- > > > > From: Mark H. Bickhard [mailto:mhb0 at Lehigh.EDU] > > > > Sent: Thursday, March 20, 2014 5:28 PM > > > > To: Kelley, Troy D CIV (US) > > > > Cc: Tsvi Achler; Andras Lorincz; bower at uthscsa.edu; > > > > connectionists at mailman.srv.cs.cmu.edu > > > > Subject: Re: Connectionists: how the brain works? > > > > > > I would agree with the importance of Sokolov habituation, but there is more > > > > than one way to understand and generalize from this phenomenon: > > > > > > http://www.lehigh.edu/~mhb0/AnticipatoryBrain20Aug13.pdf > > > > > > Mark H. Bickhard > > > > Lehigh University > > > > 17 Memorial Drive East > > > > Bethlehem, PA 18015 > > > > mark at bickhard.name > > > > http://bickhard.ws/ > > > > > > On Mar 20, 2014, at 4:41 PM, Kelley, Troy D CIV (US) wrote: > > > > > > We have found that the habituation algorithm that Sokolov discovered way > > > > back in 1963 provides an useful place to start if one is trying to determine > > > > how the brain works. The algorithm, at the cellular level, is capable of > > > > determining novelty and generating implicit predictions - which it then > > > > habituates to. Additionally, it is capable of regenerating the original > > > > response when re-exposed to the same stimuli. All of these behaviors > > > > provide an excellent framework at the cellular level for explain all sorts > > > > of high level behaviors at the functional level. And it fits the Ockham's > > > > razor principle of using a single algorithm to explain a wide variety of > > > > explicit behavior. > > > > > > Troy D. Kelley > > > > RDRL-HRS-E > > > > Cognitive Robotics and Modeling Team Leader Human Research and Engineering > > > > Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone > > > > 410-278-5869 or 410-278-6748 Note my new email address: > > > > troy.d.kelley6.civ at mail.mil > > > > > > > > > > > > > > On 3/20/14 10:41 AM, "Tsvi Achler" wrote: > > > > > > I think an Ockham's razor principle can be used to find the most > > > > optimal algorithm if it is interpreted to mean the model with the > > > > least amount of free parameters that captures the most phenomena. > > > > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.p > > > > df > > > > -Tsvi > > > > > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz > > > > wrote: > > > > Ockham works here via compressing both the algorithm and the structure. > > > > Compressing the structure to stem cells means that the algorithm > > > > should describe the development, the working, and the time dependent > > > > structure of the brain. Not compressing the description of the > > > > structure of the evolved brain is a different problem since it saves > > > > the need for the description of the development, but the working. > > > > Understanding the structure and the working of one part of the brain > > > > requires the description of its communication that increases the > > > > complexity of the description. By the way, this holds for the whole > > > > brain, so we might have to include the body at least; a structural > > > > minimist may wish to start from the genetic code, use that hint and > > > > unfold the already compressed description. There are (many and > > > > different) todos 'outside' ... > > > > > > > > Andras > > > > > > > > > > > > . > > > > > > ________________________________ > > > > From: Connectionists > > > > on behalf of james bower > > > > Sent: Thursday, March 20, 2014 3:33 AM > > > > > > To: Geoffrey Goodhill > > > > Cc: connectionists at mailman.srv.cs.cmu.edu > > > > Subject: Re: Connectionists: how the brain works? > > > > > > Geoffrey, > > > > > > Nice addition to the discussion actually introducing an interesting > > > > angle on the question of brain organization (see below) As you note, > > > > reaction diffusion mechanisms and modeling have been quite successful > > > > in replicating patterns seen in biology - especially interesting I > > > > think is the modeling of patterns in slime molds, but also for very > > > > general pattern formation in embryology. However, more and more > > > > detailed analysis of what is diffusing, what is sensing what is > > > > diffusing, and what is reacting to substances once sensed -- all > > > > linked to complex patterns of gene regulation and expression have > > > > made it clear that actual embryological development is much much more > > > > complex, as Turing himself clearly anticipated, as the quote you cite pretty > > > > clearly indicates. Clearly a smart guy. But, I don't actually think > > > > that > > > > this is an application of Ochham's razor although it might appear to > > > > be after the fact. Just as Hodgkin and Huxley were not applying it > > > > either in > > > > their model of the action potential. Turing apparently guessed (based > > > > on a > > > > lot of work at the time on pattern formation with reaction diffusion) > > > > that such a mechanism might provide the natural basis for what > > > > embryos do. Thus, just like for Hodgkin and Huxley, his model > > > > resulted from a bio-physical insight, not an explicit attempt to > > > > build a stripped down model for its own sake. I seriously doubt > > > > that Turning would have claimed that he, or his models could more > > > > effectively do what biology actually does in forming an embrio, or > > > > substitute for the actual process. > > > > > > However, I think there is another interesting connection here to the > > > > discussion on modeling the brain. Almost certainly communication and > > > > organizational systems in early living beings were reaction diffusion > > > > based. > > > > This is still a dominant effect for many 'sensing' in small organisms. > > > > Perhaps, therefore, one can look at nervous systems as structures > > > > specifically developed to supersede reaction diffusion mechanisms, > > > > thus superseding this very 'natural' but complexity limited type of > > > > communication and organization. What this means, I believe, is that > > > > a simplified or abstracted physical or mathematical model of the > > > > brain explicitly violates the evolutionary pressures responsible for > > > > its structure. Its where the wires go, what the wires do, and what > > > > the receiving neuron does with the information that forms the basis > > > > for neural computation, multiplied by a very large number. And that > > > > is dependent on the actual physical structure of those elements. > > > > > > One more point about smart guys, as a young computational > > > > neurobiologist I questioned how insightful John von Neumann actually > > > > was because I was constantly hearing about a lecture he wrote (but > > > > didn't give) at Yale suggesting that dendrites and neurons might be > > > > digital ( John von Neumann's The Computer and the Brain. (New > > > > Haven/London: Yale Univesity Press, 1958.) Very clearly a not very > > > > insightful idea for a supposedly smart guy. It wasn't until a few > > > > years later, when I actually read the lecture - that I found out that > > > > he ends by stating that this idea is almost certainly wrong, given > > > > the likely nonlinearities in neuronal dendrites. So von Neumann > > > > didn't lack insight, the people who quoted him did. It is a > > > > remarkable fact that more than 60 years later, the majority of models of > > > > so called neurons built by engineers AND neurobiologists don't consider > > > > these nonlinearities. > > > > The point being the same point, to the Hopfield, Mead, Feynman list, > > > > we can now add Turing and von Neumann as suspecting that for > > > > understanding, biology and the nervous system must be dealt with in their > > > > full complexity. > > > > > > But thanks for the example from Turing - always nice to consider actual > > > > examples. :-) > > > > > > Jim > > > > > > > > > > > > > > On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill > > > > wrote: > > > > > > Hi All, > > > > > > A great example of successful Ockham-inspired biology is Alan > > > > Turing's model for pattern formation (spots, stripes etc) in > > > > embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, > > > > 1953). Turing introduced a physical mechanism for how inhomogeneous > > > > spatial patterns can arise in a biological system from a spatially > > > > homogeneous starting point, based on the diffusion of morphogens. The > > > > paper begins: > > > > > > "In this section a mathematical model of the growing embryo will be > > > > described. This model will be a simplification and an idealization, > > > > and consequently a falsification. It is to be hoped that the features > > > > retained for discussion are those of greatest importance in the > > > > present state of knowledge." > > > > > > The paper remained virtually uncited for its first 20 years following > > > > publication, but since then has amassed 8000 citations (Google > > > > Scholar). The subsequent discovery of huge quantities of molecular > > > > detail in biological pattern formation have only reinforced the > > > > importance of this relatively simple model, not because it explains > > > > every system, but because the overarching concepts it introduced have > > > > proved to be so fertile. > > > > > > Cheers, > > > > > > Geoff > > > > > > > > On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: > > > > > > Ignoring the gross differences in circuitry between hippocampus and > > > > cerebellum, etc., is not erring on the side of simplicity, it is > > > > erring, period. Have you actually looked at a > > > > Cajal/Sxentagothai-style drawing of their circuitry? > > > > > > At 01:07 PM 3/19/2014, Brian J Mingus wrote: > > > > > > Hi Jim, > > > > > > Focusing too much on the details is risky in and of itself. Optimal > > > > compression requires a balance, and we can't compute what that > > > > balance is (all models are wrong). One thing we can say for sure is > > > > that we should err on the side of simplicity, and adding detail to > > > > theories before simpler explanations have failed is not Ockham's > > > > heuristic. That said it's still in the space of a Big Data fuzzy > > > > science approach, where we throw as much data from as many levels of > > > > analysis as we can come up with into a big pot and then construct a > > > > theory. The thing to keep in mind is that when we start pruning this > > > > model most of the details are going to disappear, because almost all > > > > of them are irrelevant. Indeed, the size of the description that > > > > includes all the details is almost infinite, whereas the length of > > > > the description that explains almost all the variance is extremely > > > > short, especially in comparison. This is why Ockham's razor is a good > > > > heuristic. It helps prevent us from wasting time on unnecessary > > > > details by suggesting that we only inquire as to the details once our > > > > existing simpler theory has failed to work. > > > > > > On 3/14/14 3:40 PM, Michael Arbib wrote: > > > > > > At 11:17 AM 3/14/2014, Juyang Weng wrote: > > > > > > The brain uses a single architecture to do all brain functions we are > > > > aware of! It uses the same architecture to do vision, audition, > > > > motor, reasoning, decision making, motivation (including pain > > > > avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). > > > > > > > > Gosh -- and I thought cerebral cortex, hippocampus and cerebellum > > > > were very different from each other. > > > > > > > > > > > > Troy D. Kelley > > > > RDRL-HRS-E > > > > Cognitive Robotics and Modeling Team Leader Human Research and Engineering > > > > Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone > > > > 410-278-5869 or 410-278-6748 Note my new email address: > > > > troy.d.kelley6.civ at mail.mil > > > > > > > > > > Classification: UNCLASSIFIED > > > > Caveats: NONE > > > > > > > > > > Stephen Grossberg > > Wang Professor of Cognitive and Neural Systems > > Professor of Mathematics, Psychology, and Biomedical Engineering > > Director, Center for Adaptive Systems http://www.cns.bu.edu/about/cas.html > > http://cns.bu.edu/~steve > > steve at bu.edu > > > > > > > > Stephen Grossberg Wang Professor of Cognitive and Neural Systems Professor of Mathematics, Psychology, and Biomedical Engineering Director, Center for Adaptive Systems http://www.cns.bu.edu/about/cas.html http://cns.bu.edu/~steve steve at bu.edu -------------- next part -------------- An HTML attachment was scrubbed... URL: From achler at gmail.com Sun Mar 23 16:25:57 2014 From: achler at gmail.com (Tsvi Achler) Date: Sun, 23 Mar 2014 13:25:57 -0700 Subject: Connectionists: how the brain works? (UNCLASSIFIED) In-Reply-To: References: <51FDF3A2-717D-4399-9076-331EEA0FC45A@lehigh.edu> Message-ID: Dear Steve, I did not say my network does not cycle between inputs and outputs, it does. However the cycling does not require cycle steps like ART: a vigilance parameter evaluated for each of the contending winners determined from a winner-take-all network. Thus it also generates a top-down expectation but does not require waiting for a winner before cycling again. In fact it does not have direct or indirect (population or mean field) connections between contending outputs. In effect, it performs a gradient descent during recognition to find neuron activation (not to learn weights but to determine the optimal activity of neurons given the current pattern). This simplifies learning (more similar to outstar learning) but also requires cycling during recognition. With respect to history, you were the speaker of honor for a conference you helped organize with final words on reviews. I was just surprised because I heard you had a hard time early in your career and were even sometimes unconventional in getting yourself heard. I look up to you and hope you would encourage and support new ideas. Sincerely, -Tsvi On Sat, Mar 22, 2014 at 7:04 PM, Stephen Grossberg wrote: > Dear Tsvi, > > You stated that ART "requires complex signals". I noted that this > statement is not correct. > > To illustrate what I meant, I noted that ART uses a simple measure of > pattern mismatch. In particular, a top-down expectation selects consistent > features and suppresses inconsistent features to focus attention upon > expected features. This property of attention is well supported by lots of > psychological and neurobiological data. > > You also contrasted models that "cycle", one being ART, with your own > network which "does not cycle". I therefore mentioned that ART hypothesis > testing and search, which involve "cycling" through operations of mismatch, > arousal, and reset, are directly supported by a lot of data. For example, > in an oddball paradigm, one can compare mismatch with properties of the > P120 ERP, arousal with properties of the N200 ERP, and reset with > properties of the P300 ERP. > > I am not sure why this reply led you to write bitterly about an old review > of one of your articles, which I know nothing about, and which is not > relevant to my specific points of information. > > Best, > > Steve > > > On Mar 22, 2014, at 4:38 PM, Tsvi Achler wrote: > > Dear Steve, > Isn't Section 8.2 exactly about the cycling (and labeled as such) and > figure 2 a depiction of the cycling? > > Your response is similar to feedback I received years ago in an evaluation > of my algorithm, where the reviewer clearly didnt read the paper in detail > but it seems was intent not to let it through because it seemed as if they > had their own algorithm and agenda. I took out snippets of that review and > placed it here because this is continuing today. > > From the review: "The network, in effect, implements a winner-takes all > scheme, when only a single output neuron reacts to each input..." > This is not true my network does not implement a Winner take all, in fact > that is the point, there is no lateral inhibition. > "... this network type can be traced back to the sixties, most notably to > the work of Grossberg ... As a result, I do not see any novelty in this > paper ... Overall recommendation: 1 (Reject) .. Reviewer's confidence: 5 > (highest)." > > This is exactly what I mean when I stated that it seems academia would > rather bury new ideas. Such a callous and strong dismissal is devastating > to a young student and detrimental to the field. > Someone as decorated and established as you has the opportunity to move > the field forward. However instead the neural network aspect of feedback > during recognition is being actively inhibited by unsubstantive and > destructive efforts. > > I would be happy to work with you offline to write a joint statement on > this with all of the technical details. > > Sincerely, > -Tsvi > > > On Mar 22, 2014 4:08 AM, "Stephen Grossberg" wrote: > > > > Dear Tsvi, > > > > You mention Adaptive Resonance below and suggest that it "requires > complex signals indicating when to stop, compare, and cycle". That is not > correct. > > > > ART uses a simple measure of pattern mismatch. Moreover, psychological, > neurophysiological, anatomical, and ERP data support the operations that it > models during hypothesis testing and memory search. ART predicted various > of these data before they were collected. > > > > If you would like to pursue this further, see > http://cns.bu.edu/~steve/ART.pdf for a recent heuristic review. > > > > Best, > > > > Steve > > > > > > On Mar 21, 2014, at 10:29 PM, Tsvi Achler wrote: > > > > Sorry for the length of this response but I wanted to go into some > > detail here. > > > > I see the habituation paradigm as somewhat analogous to surprise and > > measurement of error during recognition. I can think of a few > > mathematical Neural Network classifiers that can generate an internal > > pattern for match during recognition to calculate this > > habituation/surprise orientation. Feedforward networks definitely > > will not work because they don't recall the internal stimulus very > > well. One option is adaptive resonance (which I assume you use), but > > it cycles through the patterns one at a time and requires complex > > signals indicating when to stop, compare, and cycle. I assume > > Juyang's DN can also do something similar but I suspect it also must > > cycle since it also has lateral inhibition. Bidirectional Associative > > Memories (BAM) may also be used. Others such as Bayes networks and > > free-energy principle can used, although they are not as easily > > translatable to neural networks. > > > > Another option is a network like mine which does not have lateral > > connections but also generates internal patterns. The advantage is > > that it can also generate mixtures of patterns at once, does not cycle > > through individual patterns, does not require signals associated with > > cycling, and can be shown mathematically to be analogous to > > feedforward networks. The error signal it produces can be used for an > > orientation reflex or what I rather call attention. It is essential > > for recognition and planning. > > > > I would be happy to give a talk on this and collaborate on a rigorous > > comparison. Indeed it is important to look at models other than those > > using feedforward connections during recognition. > > > > Sincerely, > > > > -Tsvi > > > > > > > > > > On Mar 21, 2014 5:25 AM, "Kelley, Troy D CIV (US)" > > wrote: > > > > > > Classification: UNCLASSIFIED > > > > Caveats: NONE > > > > > > Yes, Mark, I would argue that habituation is anticipatory prediction. > The > > > > neuron creates a model of the incoming stimulus and the neuron is > > > > essentially predicting that the next stimuli will be comparatively > similar > > > > to the previous stimulus. If this prediction is met, the neuron > habituates. > > > > That is a simple, low level, predictive model. > > > > > > -----Original Message----- > > > > From: Mark H. Bickhard [mailto:mhb0 at Lehigh.EDU] > > > > Sent: Thursday, March 20, 2014 5:28 PM > > > > To: Kelley, Troy D CIV (US) > > > > Cc: Tsvi Achler; Andras Lorincz; bower at uthscsa.edu; > > > > connectionists at mailman.srv.cs.cmu.edu > > > > Subject: Re: Connectionists: how the brain works? > > > > > > I would agree with the importance of Sokolov habituation, but there is > more > > > > than one way to understand and generalize from this phenomenon: > > > > > > http://www.lehigh.edu/~mhb0/AnticipatoryBrain20Aug13.pdf > > > > > > Mark H. Bickhard > > > > Lehigh University > > > > 17 Memorial Drive East > > > > Bethlehem, PA 18015 > > > > mark at bickhard.name > > > > http://bickhard.ws/ > > > > > > On Mar 20, 2014, at 4:41 PM, Kelley, Troy D CIV (US) wrote: > > > > > > We have found that the habituation algorithm that Sokolov discovered way > > > > back in 1963 provides an useful place to start if one is trying to > determine > > > > how the brain works. The algorithm, at the cellular level, is capable of > > > > determining novelty and generating implicit predictions - which it then > > > > habituates to. Additionally, it is capable of regenerating the original > > > > response when re-exposed to the same stimuli. All of these behaviors > > > > provide an excellent framework at the cellular level for explain all > sorts > > > > of high level behaviors at the functional level. And it fits the > Ockham's > > > > razor principle of using a single algorithm to explain a wide variety of > > > > explicit behavior. > > > > > > Troy D. Kelley > > > > RDRL-HRS-E > > > > Cognitive Robotics and Modeling Team Leader Human Research and > Engineering > > > > Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone > > > > 410-278-5869 or 410-278-6748 Note my new email address: > > > > troy.d.kelley6.civ at mail.mil > > > > > > > > > > > > > > On 3/20/14 10:41 AM, "Tsvi Achler" wrote: > > > > > > I think an Ockham's razor principle can be used to find the most > > > > optimal algorithm if it is interpreted to mean the model with the > > > > least amount of free parameters that captures the most phenomena. > > > > http://reason.cs.uiuc.edu/tsvi/Evaluating_Flexibility_of_Recognition.p > > > > df > > > > -Tsvi > > > > > > On Wed, Mar 19, 2014 at 10:37 PM, Andras Lorincz > > > > wrote: > > > > Ockham works here via compressing both the algorithm and the structure. > > > > Compressing the structure to stem cells means that the algorithm > > > > should describe the development, the working, and the time dependent > > > > structure of the brain. Not compressing the description of the > > > > structure of the evolved brain is a different problem since it saves > > > > the need for the description of the development, but the working. > > > > Understanding the structure and the working of one part of the brain > > > > requires the description of its communication that increases the > > > > complexity of the description. By the way, this holds for the whole > > > > brain, so we might have to include the body at least; a structural > > > > minimist may wish to start from the genetic code, use that hint and > > > > unfold the already compressed description. There are (many and > > > > different) todos 'outside' ... > > > > > > > > Andras > > > > > > > > > > > > . > > > > > > ________________________________ > > > > From: Connectionists > > > > on behalf of james bower > > > > Sent: Thursday, March 20, 2014 3:33 AM > > > > > > To: Geoffrey Goodhill > > > > Cc: connectionists at mailman.srv.cs.cmu.edu > > > > Subject: Re: Connectionists: how the brain works? > > > > > > Geoffrey, > > > > > > Nice addition to the discussion actually introducing an interesting > > > > angle on the question of brain organization (see below) As you note, > > > > reaction diffusion mechanisms and modeling have been quite successful > > > > in replicating patterns seen in biology - especially interesting I > > > > think is the modeling of patterns in slime molds, but also for very > > > > general pattern formation in embryology. However, more and more > > > > detailed analysis of what is diffusing, what is sensing what is > > > > diffusing, and what is reacting to substances once sensed -- all > > > > linked to complex patterns of gene regulation and expression have > > > > made it clear that actual embryological development is much much more > > > > complex, as Turing himself clearly anticipated, as the quote you cite > pretty > > > > clearly indicates. Clearly a smart guy. But, I don't actually think > > > > that > > > > this is an application of Ochham's razor although it might appear to > > > > be after the fact. Just as Hodgkin and Huxley were not applying it > > > > either in > > > > their model of the action potential. Turing apparently guessed (based > > > > on a > > > > lot of work at the time on pattern formation with reaction diffusion) > > > > that such a mechanism might provide the natural basis for what > > > > embryos do. Thus, just like for Hodgkin and Huxley, his model > > > > resulted from a bio-physical insight, not an explicit attempt to > > > > build a stripped down model for its own sake. I seriously doubt > > > > that Turning would have claimed that he, or his models could more > > > > effectively do what biology actually does in forming an embrio, or > > > > substitute for the actual process. > > > > > > However, I think there is another interesting connection here to the > > > > discussion on modeling the brain. Almost certainly communication and > > > > organizational systems in early living beings were reaction diffusion > > > > based. > > > > This is still a dominant effect for many 'sensing' in small organisms. > > > > Perhaps, therefore, one can look at nervous systems as structures > > > > specifically developed to supersede reaction diffusion mechanisms, > > > > thus superseding this very 'natural' but complexity limited type of > > > > communication and organization. What this means, I believe, is that > > > > a simplified or abstracted physical or mathematical model of the > > > > brain explicitly violates the evolutionary pressures responsible for > > > > its structure. Its where the wires go, what the wires do, and what > > > > the receiving neuron does with the information that forms the basis > > > > for neural computation, multiplied by a very large number. And that > > > > is dependent on the actual physical structure of those elements. > > > > > > One more point about smart guys, as a young computational > > > > neurobiologist I questioned how insightful John von Neumann actually > > > > was because I was constantly hearing about a lecture he wrote (but > > > > didn't give) at Yale suggesting that dendrites and neurons might be > > > > digital ( John von Neumann's The Computer and the Brain. (New > > > > Haven/London: Yale Univesity Press, 1958.) Very clearly a not very > > > > insightful idea for a supposedly smart guy. It wasn't until a few > > > > years later, when I actually read the lecture - that I found out that > > > > he ends by stating that this idea is almost certainly wrong, given > > > > the likely nonlinearities in neuronal dendrites. So von Neumann > > > > didn't lack insight, the people who quoted him did. It is a > > > > remarkable fact that more than 60 years later, the majority of models of > > > > so called neurons built by engineers AND neurobiologists don't consider > > > > these nonlinearities. > > > > The point being the same point, to the Hopfield, Mead, Feynman list, > > > > we can now add Turing and von Neumann as suspecting that for > > > > understanding, biology and the nervous system must be dealt with in their > > > > full complexity. > > > > > > But thanks for the example from Turing - always nice to consider actual > > > > examples. :-) > > > > > > Jim > > > > > > > > > > > > > > On Mar 19, 2014, at 8:30 PM, Geoffrey Goodhill > > > > wrote: > > > > > > Hi All, > > > > > > A great example of successful Ockham-inspired biology is Alan > > > > Turing's model for pattern formation (spots, stripes etc) in > > > > embryology (The chemical basis of morphogenesis, Phil Trans Roy Soc, > > > > 1953). Turing introduced a physical mechanism for how inhomogeneous > > > > spatial patterns can arise in a biological system from a spatially > > > > homogeneous starting point, based on the diffusion of morphogens. The > > > > paper begins: > > > > > > "In this section a mathematical model of the growing embryo will be > > > > described. This model will be a simplification and an idealization, > > > > and consequently a falsification. It is to be hoped that the features > > > > retained for discussion are those of greatest importance in the > > > > present state of knowledge." > > > > > > The paper remained virtually uncited for its first 20 years following > > > > publication, but since then has amassed 8000 citations (Google > > > > Scholar). The subsequent discovery of huge quantities of molecular > > > > detail in biological pattern formation have only reinforced the > > > > importance of this relatively simple model, not because it explains > > > > every system, but because the overarching concepts it introduced have > > > > proved to be so fertile. > > > > > > Cheers, > > > > > > Geoff > > > > > > > > On Mar 20, 2014, at 6:27 AM, Michael Arbib wrote: > > > > > > Ignoring the gross differences in circuitry between hippocampus and > > > > cerebellum, etc., is not erring on the side of simplicity, it is > > > > erring, period. Have you actually looked at a > > > > Cajal/Sxentagothai-style drawing of their circuitry? > > > > > > At 01:07 PM 3/19/2014, Brian J Mingus wrote: > > > > > > Hi Jim, > > > > > > Focusing too much on the details is risky in and of itself. Optimal > > > > compression requires a balance, and we can't compute what that > > > > balance is (all models are wrong). One thing we can say for sure is > > > > that we should err on the side of simplicity, and adding detail to > > > > theories before simpler explanations have failed is not Ockham's > > > > heuristic. That said it's still in the space of a Big Data fuzzy > > > > science approach, where we throw as much data from as many levels of > > > > analysis as we can come up with into a big pot and then construct a > > > > theory. The thing to keep in mind is that when we start pruning this > > > > model most of the details are going to disappear, because almost all > > > > of them are irrelevant. Indeed, the size of the description that > > > > includes all the details is almost infinite, whereas the length of > > > > the description that explains almost all the variance is extremely > > > > short, especially in comparison. This is why Ockham's razor is a good > > > > heuristic. It helps prevent us from wasting time on unnecessary > > > > details by suggesting that we only inquire as to the details once our > > > > existing simpler theory has failed to work. > > > > > > On 3/14/14 3:40 PM, Michael Arbib wrote: > > > > > > At 11:17 AM 3/14/2014, Juyang Weng wrote: > > > > > > The brain uses a single architecture to do all brain functions we are > > > > aware of! It uses the same architecture to do vision, audition, > > > > motor, reasoning, decision making, motivation (including pain > > > > avoidance and pleasure seeking, novelty seeking, higher emotion, etc.). > > > > > > > > Gosh -- and I thought cerebral cortex, hippocampus and cerebellum > > > > were very different from each other. > > > > > > > > > > > > Troy D. Kelley > > > > RDRL-HRS-E > > > > Cognitive Robotics and Modeling Team Leader Human Research and > Engineering > > > > Directorate U.S. Army Research Laboratory Aberdeen, MD 21005 Phone > > > > 410-278-5869 or 410-278-6748 Note my new email address: > > > > troy.d.kelley6.civ at mail.mil > > > > > > > > > > Classification: UNCLASSIFIED > > > > Caveats: NONE > > > > > > > > > > Stephen Grossberg > > Wang Professor of Cognitive and Neural Systems > > Professor of Mathematics, Psychology, and Biomedical Engineering > > Director, Center for Adaptive Systems > http://www.cns.bu.edu/about/cas.html > > http://cns.bu.edu/~steve > > steve at bu.edu > > > > > > > > > > > Stephen Grossberg > Wang Professor of Cognitive and Neural Systems > Professor of Mathematics, Psychology, and Biomedical Engineering > Director, Center for Adaptive Systems http://www.cns.bu.edu/about/cas.html > http://cns.bu.edu/~steve > steve at bu.edu > > > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From AdvancedAnalytics at uts.edu.au Sun Mar 23 20:01:47 2014 From: AdvancedAnalytics at uts.edu.au (Advanced Analytics) Date: Mon, 24 Mar 2014 11:01:47 +1100 Subject: Connectionists: REMINDER - AAi Public Short Course - 'Recommendation Systems - an Introduction' - Thursday 3 April 2014 Message-ID: <8112393AA53A9B4A9BDDA6421F26C68A0173A2CFE137@MAILBOXCLUSTER.adsroot.uts.edu.au> Dear Colleague, REMINDER - AAi Public Short Course - 'Recommendation Systems - an Introduction' - Thursday 3 April 2014 https://shortcourses-bookings.uts.edu.au/Clientview/Schedules/ScheduleDetail.aspx?ScheduleID=1588&EventID=1325 AA's short course 'Recommendation Systems - an Introduction' may be of interest to you or others in your organisation or network. * Have you ever been amazed that the products an online retailer suggested to you are just what you want to buy? Do you know what tricks are played in the background to enable such intelligent recommendations? These intelligent recommendations are due to the recommender system * Nowadays, the mainstream technology for personalized recommendation is collaborative filtering (CF). Collaborative filtering is a computational approach, which predicts a user's preference of an item by finding like-minded users based on their historical records * To introduce this rising technology to a broad audience from academics and industries, we propose a one-day short course, including four sessions: 1) Overview, 2) Techniques, 3) Challenges, and 4) Case studies and lab exercises. This program is particularly useful for: All those involved in BIG DATA for their organisation: * Students * Researchers * Academics * Industry Practitioners Program topics: * 9:00 am - 10:15am: RS Overview - Dr Guandong Xu * 10:30 am - 12:15pm: State-of-the-art techniques in RS - Dr Bin Li * 1:00pm - 2:15: Challenges and emerging Techniques in RS - Dr Shlomo Berkovsky * 2:15pm - 4:30pm: Recommendation Engine Case studies and lab exercise - Dr Guandong Xu Short Course outcomes: Upon completion of this course students will: * Understand the fundamental concepts and techniques of RS * Get the insight of main stream CF mechanism, and know the key challenges in RS * Be aware of new and poetical techniques emerging in RS, such as social recommendation * Be able to use Apache Mahout to implement a toy recommendation engine Please register here https://shortcourses-bookings.uts.edu.au/Clientview/Schedules/ScheduleDetail.aspx?ScheduleID=1588&EventID=1325 An important foundation short course in the AAI series of advanced data analytic short courses - please view this short course and others here http://www.uts.edu.au/research-and-teaching/our-research/advanced-analytics-institute-p2/short-courses/aai-education-and-0 Please note we also design and deliver in house corporate training on Advanced Data Analytics. We are happy to discuss at your convenience. Thank you and regards. Colin Wise Operations Manager Faculty of Engineering & IT The Advanced Analytics Institute [cid:image001.jpg at 01CF3ED6.6032D020] University of Technology, Sydney Blackfriars Campus Building 2, Level 1 Tel. +61 2 9514 9267 M. 0448 916 589 Email: Colin.Wise at uts.edu.au AAI: www.analytics.uts.edu.au/ Reminder - AAI Short Course - Behaviour Analytics - an Introduction on Tuesday 29 April 2014 https://shortcourses-bookings.uts.edu.au/ClientView/Schedules/ScheduleDetail.aspx?ScheduleID=1572&EventID=1294 AAI Education and Training Short Courses Survey - you may be interested in completing our AAI Survey at http://analytics.uts.edu.au/shortcourses/survey.html AAI Email Policy - should you wish to not receive this periodic communication on Data Analytics Learning please reply to our email (to sender) with UNSUBSCRIBE in the Subject. We will delete you from our database. Thank you for your past and future support. UTS CRICOS Provider Code: 00099F DISCLAIMER: This email message and any accompanying attachments may contain confidential information. If you are not the intended recipient, do not read, use, disseminate, distribute or copy this message or attachments. If you have received this message in error, please notify the sender immediately and delete this message. Any views expressed in this message are those of the individual sender, except where the sender expressly, and with authority, states them to be the views of the University of Technology Sydney. Before opening any attachments, please check them for viruses and defects. Think. Green. Do. Please consider the environment before printing this email. -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image001.jpg Type: image/jpeg Size: 4385 bytes Desc: image001.jpg URL: From jerryzhu at cs.wisc.edu Sun Mar 23 22:36:59 2014 From: jerryzhu at cs.wisc.edu (Jerry Zhu) Date: Sun, 23 Mar 2014 21:36:59 -0500 (CDT) Subject: Connectionists: (Deadline Extended to April 4) ICML 2014 Workshop on Topological Methods for Machine Learning Message-ID: (Deadline Extended to April 4) ICML Workshop on Topological Methods for Machine Learning June 2014, Beijing, China http://topology.cs.wisc.edu This workshop aims to translate advances in computational topology (e.g., homology, cohomology, persistence, Hodge theory) into machine learning algorithms and applications. Topology has the potential to be a new mathematical tool for machine learning. We expect the workshop to bring topologists, statisticians and machine learning researchers closer to realize this potential. Computational topology saw three major developments in recent years: persistent homology, Euler calculus and Hodge theory. Persistent homology extracts stable homology groups against noise; Euler Calculus encodes integral geometry and is easier to compute than persistent homology or Betti numbers; Hodge theory connects geometry to topology via optimization and spectral method. All three techniques are related to Morse theory, which is inspiring new computational tools or algorithms for data analysis. Computational topology has inspired a number of applications in the last few years, including game theory, graphics, image processing, multimedia, neuroscience, numerical PDE, peridynamics, ranking, robotics, voting theory, sensor networks, and natural language processing. Which promising directions in computational topology can mathematicians and machine learning researchers work on together, in order to develop new models, algorithms, and theory for machine learning? While all aspects of computational topology are appropriate for this workshop, our emphasis is on topology applied to machine learning -- concrete models, algorithms and real-world applications. Topics We seek papers in all areas where topology and machine learning interact, especially on translating computational topology into new machine learning algorithms and applications. Topics include, but are not limited to, the following: - Models in machine learning where topology plays an important role; - Applications of topology in all areas related to machine learning and human cognition; - Statistical properties for topological inference; - Algorithms based on computational topology; - Feature extraction with topological methods. Submissions Papers should be 4-page (excluding references) extended abstracts on topics relevant to the workshop. Papers must be formatted in ICML style following this webpage: http://icml.cc/2014/14.html. Please email PDF submissions to topologyicml2014 at gmail.com. Submissions due date: 4/4/14 (extended) Authors notification: 4/18/2014 Organizers Lek-Heng Lim, University of Chicago Yuan Yao, Peking University Jerry Zhu, University of Wisconsin-Madison Jun Zhu, Tsinghua University Questions and comments can be directed to topologyicml2014 at gmail.com. From demian.battaglia at univ-amu.fr Mon Mar 24 06:09:01 2014 From: demian.battaglia at univ-amu.fr (Demian Battaglia) Date: Mon, 24 Mar 2014 11:09:01 +0100 Subject: Connectionists: First neural connectomics challenge: from imaging to connectivity Message-ID: <9DDF72A8-3504-4DF2-AAE9-5D60B40BE69B@univ-amu.fr> The brain contains nearly 100 billion neurons with an average 7000 synaptic connections. Recovering the exact wiring of the brain (connectome) at this neural level is therefore a daunting task. Traditional neuroanatomical methods of axonal tracing or electrophysiological direct assays cannot scale up to very large networks. Could there be alternative methods, such as inference algorithms, to recovering neural network structures from patterns of neural activity? This challenge (inaugurating a series of future "Connectomics" challenges) will stimulate research on network structure inference from calcium fluorescence data, including causal discovery methods. WHAT YOU GET: Time series of the activity of 1000 neurons in a culture (simulated calcium fluorescence data) WHAT YOU PREDICT: The directed connections between neurons. 45 days to go, 3000 $ allocated for the top three ranked competitors (if they accept to make an open release of their code ;-) Challenge submission website: https://www.kaggle.com/c/connectomics Over 70 teams are already attending! There will be $3000 in prizes awarded. We are an official challenge at IEEE WCCI 2014 meeting in Beijing, where challenge results will be spotlighted: http://www.ieee-wcci2014.org (see "accepted competitions") Participants may present their results at a dedicated workshop at ECML 2014 (Nancy, France): http://www.ecmlpkdd2014.org (see "discovery challenges") http://connectomics.chalearn.org/workshop (workshop webpage) The top-ranked challenge participants and authors of the best workshop papers will be invited to participate to full length submissions (individual or collective, including detailed post-challenge analytics and algorithm comparisons and benchmarking) to peer-reviewed journals, such as Frontiers in Neuroinformatics or PLoS Computational Biology. Calendar: May 5, 2014: End of challenge. May 20, 2014: All teams must turn in fact sheets. The three top ranking teams must turn in their code to qualify for prizes. The post-challenge verifications start. June 1, 2014: End of the post challenge verifications. Release of the official ranking. June 20, 2014: ECML workshop proceeding paper submission deadline. July 6-11, 2014: Result presentation at WCCI 2014, Beijing, China. August 30, 2014: Revised papers due for ECML workshop proceedings. Beginning of post-challenge papers design. September 15 or 19, 2014: ECML workshop, Nancy, France. Discussion of the results and preparation of post-challenge synthesis. For any information or request, please contact: Demian Battaglia, demian.battaglia at univ-amu.fr --- Demian Battaglia, PhD Institut de Neurosciences des Syst?mes, Aix-Marseille Universit? - Marseille, France Bernstein Center for Computational Neuroscience - G?ttingen, Germany http://www.nld.ds.mpg.de/~demian From fmschleif at googlemail.com Mon Mar 24 05:55:44 2014 From: fmschleif at googlemail.com (Frank-Michael Schleif) Date: Mon, 24 Mar 2014 09:55:44 +0000 Subject: Connectionists: 10th Workshop on Self-Organizing Maps 2014 - WSOM 2014 (program, accepted papers, registration - online) Message-ID: We apologize for possible duplicates of this message sent to distribution lists. 10th Workshop on Self-Organizing Maps 2014 - WSOM 2014 Mittweida, Germany, 2-4 July 2014 http://www.wsom2014.de/ The Workshop on Self-Organizing Maps 2014 -- WSOM 2014 will be held in the beautiful small town Mittweida located close to the mountains Erzgebirge in Saxony/Germany. It will bring together researchers and practitioners in the field of self-organizing systems for data analysis, with a particular emphasis on self-organizing maps and learning vector quantization. WSOM 2014 is the 10th conference in a series of biannual international conferences started with WSOM'97 in Helsinki. The workshop is focusing on recent advancements in the field of self-organizing map, learning vector quantization and prototype based learning. In a broader scope the workshop also covers practical approaches of these techniques and other machine learning methods. This year session are * Self-Organizing Maps Theory and Visualization * Prototype Based Classification * Classification and non-standard metric approaches * Applications Invited Speakers: * Prof. Dr. Michael Biehl, University Groningen (NL), Johann-Bernoulli-Institute of Mathematics and Computer Sciences * Prof. Dr. Erzs?bet Mer?nyi, Rice University Houston (USA), Department of Statistics and Department of Electrical and Computer Engineering * Prof. Dr. Fabrice Rossi, Universit? Paris1 - Panth?on-Sorbonne, Department Statistique, Analyse, Mod?lisation Multidisciplinaire (SAMM) (F) Preliminary program The preliminary program of the WSOM 2014 conference is now available on the Web: http://www.global.hs-mittweida.de/~wsom2014/wsom2014_program.htm Further the list of -- accepted papers -- is now online http://www.global.hs-mittweida.de/~wsom2014/wsom2014_accepted_papers.htm You can register (early bird until 31.05) at http://www.global.hs-mittweida.de/~wsom2014/wsom2014_registration.htm Further details at: www.wsom2014.de Further details (registration, venue a.s.o), at: www.wsom2014.de You can also contact wsom2014 at hs-mittweida.de -- ------------------------------------------------------- Dr. rer. nat. habil. Frank-Michael Schleif School of Computer Science The University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom - email: fschleif at techfak.uni-bielefeld.de http://promos-science.blogspot.de/ ------------------------------------------------------- From kerstin at nld.ds.mpg.de Tue Mar 25 05:15:02 2014 From: kerstin at nld.ds.mpg.de (Kerstin Mosch) Date: Tue, 25 Mar 2014 10:15:02 +0100 Subject: Connectionists: Call for Abstracts: Bernstein Conference 2014 Message-ID: <53314586.8080605@nld.ds.mpg.de> An HTML attachment was scrubbed... URL: From gros at itp.uni-frankfurt.de Tue Mar 25 11:40:36 2014 From: gros at itp.uni-frankfurt.de (Prof. Claudius Gros) Date: Tue, 25 Mar 2014 16:40:36 +0100 (CET) Subject: Connectionists: Fully funded PhD position: computational neuroscience / complex system theory Message-ID: I would like to bring your attention to our PhD-program in Complex Dynamical Systems Theory / Computational Neurosciences At the Institute for Theoretical Physics, Goethe University Frankfurt Field(s): complex systems theory, computational neurosciences neural models and networks, dynamical systems Application deadline: April 27, 2014 Supervisor: Prof. Dr. Claudius Gros E-mail: cgr at itp.uni-frankfurt.de Address: Institute for Theoretical Physics, Goethe University Frankfurt, Job description: Applications are invited for a fully funded PhD position at the Institute for Theoretical Physics, Frankfurt University. We are developing new models and generative principles for the brain using a range of toolsets from dynamical systems theory and computational neurosciences. Examples are new objective functions and generating functionals, attractor metadynamics, transient state dynamics and self-limiting Hebbian plasticity rules. Several subjects are available for the announced PhD thesis including studies of the autonomous brain dynamics and/or studies of new synaptic plasticity rules, like trace formulations of STDP and the interplay between short term synaptic plasticity and neural dynamics. The work will include analytical investigations and numerical simulations of neural models and neural networks, using the toolset of dynamical systems theory. The candidates should have a Diploma/Master in physics with an excellent academic track record and good computational skills. Experience or strong interest in the fields of complex systems, computational neurosciences, dynamical systems theory and/or artificial or biological cognitive systems is expected. The degree of scientific research experience is expected to be on the level of a German Diploma/Master. The appointments will start summer 2014, for up to three years. Interested applicants should submit a curriculum vitae, a list of publications and arrange for two letters of reference to be sent to the address below. Prof. Dr. C. Gros Institute for Theoretical Physics Goethe University Frankfurt Max-von-Laue-Str. 1 60438 Frankfurt am Main Germany cgr at itp.uni-frankfurt.de http://itp.uni-frankfurt.de/~gros ***************************************** *** Prof. Dr. Claudius Gros *** *** +49 (0)69 798 47818 *** *** http://itp.uni-frankfurt.de/~gros *** ***************************************** -------------------------------------------------------- --- Complex and Adaptive Dynamical Systems, A Primer --- --- A graduate-level textbook, Springer (2008/10/13) --- -------------------------------------------------------- -------------- next part -------------- A non-text attachment was scrubbed... Name: PhDComplexDynamSyst_2014.pdf Type: application/pdf Size: 111695 bytes Desc: PhDComplexDynamSyst_2014.pdf URL: From julian at togelius.com Tue Mar 25 18:19:49 2014 From: julian at togelius.com (Julian Togelius) Date: Tue, 25 Mar 2014 23:19:49 +0100 Subject: Connectionists: Submission reminder for CIG 2014 - a great place to send papers on neural networks / machine learning applied to games Message-ID: Computational Intelligence and Games 2014 in Dortmund, Germany conference dates: August 26 to 29, 2014 http://cig2014.de/ ** 6 DAYS TO GO, submission deadline April 1 ** keynotes: Rilla Khaled, University of Malta Mark Riedl, Georgia Institute of Technology Thorsten Quandt, WWU Muenster Jochen Peketz, Blue Byte GmbH tutorial: (call still open until April 1) A Panorama of Research on CI and AI in Games, Julian Togelius and Georgios Yannakakis competitions: Fighting Game Competition Human-Like Bots Competition General Video Game Playing Starcraft AI Competition (competition paper deadline will be in 1st half of May) Guenter Rudolph and Mike Preuss, General Chairs -- Julian Togelius Associate Professor IT University of Copenhagen Rued Langgaards Vej 7, 2300 Copenhagen, Denmark mail: julian at togelius.com, web: http://julian.togelius.com mobile: +46-705-192088, office: +45-7218-5277 From m.lengyel at eng.cam.ac.uk Wed Mar 26 09:44:18 2014 From: m.lengyel at eng.cam.ac.uk (=?windows-1252?Q?M=E1t=E9_Lengyel?=) Date: Wed, 26 Mar 2014 14:44:18 +0100 Subject: Connectionists: Advanced Course in Computational Neuroscience 2014 Message-ID: ADVANCED COURSE IN COMPUTATIONAL NEUROSCIENCE August 3 - 30, 2014, FIAS, Frankfurt, Germany http://fias.uni-frankfurt.de/accn/ *** NOTE EXTENDED DEADLINE FOR APPLICATIONS *** Applications accepted: February 10, 2014 ? March 31, 2014 SCIENTIFIC DIRECTORS: * Ehud Ahissar (Weizmann Institute, Israel) * Dieter Jaeger (Emory University, USA) * M?t? Lengyel (University of Cambridge, UK) * Christian Machens (Champalimaud Neuroscience Programme, Portugal) LOCAL ORGANIZERS: * Jochen Triesch (FIAS, Frankfurt, Germany) * Hermann Cuntz (FIAS & ESI, Frankfurt, Germany) FUNDING: FENS, IBRO, FIAS, MPI Brain Research, Hertie Foundation The ACCN is for advanced graduate students and postdoctoral fellows who are interested in learning the essentials of the field of computational neuroscience. The course has two complementary parts. Mornings are devoted to lectures given by distinguished international faculty on topics across the breadth of experimental and computational neuroscience. During the rest of the day, students pursue a project of their choosing under the close supervision of expert tutors. This gives them practical training in the art and practice of neural modeling. The first week of the course introduces students to essential neurobiological concepts and to the most important techniques in modeling single cells, synapses and circuits. Students learn how to solve their research problems using software such as MATLAB, NEST, NEURON, Python, XPP, etc. During the following three weeks the lectures cover networks and specific neural systems and functions. Topics range from modeling single cells and subcellular processes through the simulation of simple circuits, large neuronal networks and system level models of the brain. The course ends with project presentations by the students. The course is designed for students from a variety of disciplines, including neuroscience, physics, electrical engineering, computer science, mathematics and psychology. Students are expected to have a keen interest and basic background in neurobiology as well as some computer experience. Students of any nationality can apply. Essential details: * Course size: about 30 students. * Fee (which covers tuition, lodging, meals and excursions): EUR 750. * Scholarships and travel stipends are available. * Application start: February 10, 2014 * Application deadline: March 23, 2014 * Deadline for letters of recommendation: March 23, 2014 * Notification of results: May, 2014 Information and application http://fias.uni-frankfurt.de/accn/ Contact address: accn at fias.uni-frankfurt.de FACULTY Erik De Schutter (Okinawa Institute of Science and Technology, Japan), Dieter Jaeger (Emory University, USA), Astrid Prinz (Emory University, USA), Charles Wilson (University of Texas, San Antonio, USA), Michael Hausser (University College London, UK), Sophie Deneve (Ecole Normale Superieure, France), Christian Machens (Champalimaud Centre for the Unknown, Portugal), Jochen Triesch (FIAS, Germany), Misha Tsodyks (Weizmann Institute, Israel), Carl van Vreeswijk (CNRS Paris, France), Peter Dayan (University College London, UK), Joern Diedrichsen (University College London, UK), M?t? Lengyel (University of Cambridge, UK), Zhaoping Li (University College London, UK), Tatjana Tchumatchenko (MPI for Brain Research, Frankfurt, Germany), Ehud Ahissar (Weizmann Institute, Israel), Merav Ahissar (Hebrew University, Israel), Nava Rubin (Universitat Pompeu Fabra, Spain) General Interest Lectures: Hans-Joachim Pflueger (Freie Universitaet Berlin, Germany), Erin Schuman (MPI for Brain Research, Germany), Erik De Schutter (Okinawa Institute of Science and Technology, Japan), J. Kevin O'Regan (Paris Descartes University, France) Tutors: Daniel Miner (Frankfurt, Germany), Andreea Lazar (Frankfurt, Germany), Wieland Brendel (Lisbon / Tuebingen, Portugal / Germany), Sina Tootoonian (Cambridge, UK), Peter Jedlicka (Frankfurt, Germany) SECRETARY DURING THE COURSE Chris Ploegaert (University of Antwerp, Belgium) -- Mate Lengyel, PhD Computational and Biological Learning Lab Cambridge University Engineering Department Trumpington Street, Cambridge CB2 1PZ, UK tel: +44 (0)1223 748 532, fax: +44 (0)1223 332 662 email: m.lengyel at eng.cam.ac.uk web: www.eng.cam.ac.uk/~m.lengyel From M.M.vanZaanen at uvt.nl Wed Mar 26 11:54:41 2014 From: M.M.vanZaanen at uvt.nl (Menno van Zaanen) Date: Wed, 26 Mar 2014 16:54:41 +0100 Subject: Connectionists: 2nd CFP COLING Workshop on Computational Approaches to Compound Analysis (ComAComA) Message-ID: <20140326155441.GX3652@pinball.uvt.nl> Second Call for Papers The First Workshop on Computational Approaches to Compound Analysis (ComAComA 2014) at COLING 2014 Dublin, Ireland, 23/24 August, 2014 DESCRIPTION The ComAComA workshop is an interdisciplinary platform for researchers to present recent and ongoing work on compound processing in different languages. Given the high productivity of compounding in a wide range of languages, compound processing is an interesting subject in linguistics, computational linguistics, and other applied disciplines. For example, for many language technology applications, compound processing remains a challenge (both morphologically and semantically), since novel compounds are created and interpreted on the fly. In order to deal with this productivity, systems that can analyse new compound forms and their meanings need to be developed. From an interdisciplinary perspective, we also need to better understand the process of compounding (for instance, as a cognitive process), in order to model its complexity. The workshop has several related aims. Firstly, it will bring together researchers from different research fields (e.g., computational linguistics, linguistics, neurolinguistics, psycholinguistics, language technology) to discuss various aspects of compound processing. Secondly, the workshop will provide an overview of the current state-of-the-art research, as well as desired resources for future research in this area. Finally, we expect that the interdisciplinary nature of the workshop will result in methodologies to evaluate compound processing systems from different perspectives. KEYNOTE SPEAKERS Diarmuid ?? S??aghdha (University of Cambridge) Andrea Krott (University of Birmingham) TOPICS OF INTEREST The ComAComA workshop solicits papers on original and unpublished research on the following topics, including, but not limited to: ** Annotation of compounds for computational purposes ** Categorisation of compounds (e.g. different taxonomies) ** Classification of compound semantics ** Compound splitting ** Automatic morphological analysis of compounds ** Compound processing in computational psycholinguistics ** Psycho- and/or neurolinguistic aspects of compound processing ** Theoretical and/or descriptive linguistic aspects of compound processing ** Compound paraphrase generation ** Applications of compound processing ** Resources for compound processing ** Evaluation methodologies for compound processing PAPER REQUIREMENTS ** Papers should describe original work, with room for completed work, well-advanced ongoing research, or contemplative, novel ideas. Papers should indicate clearly the state of completion of the reported results. Wherever appropriate, concrete evaluation results should be included. ** Submissions will be judged on correctness, originality, technical strength, significance and relevance to the conference, and interest to the attendees. ** Submissions presented at the conference should mostly contain new material that has not been presented at any other meeting with publicly available proceedings. Papers that are being submitted in parallel to other conferences or workshops must indicate this on the title page, as must papers that contain significant overlap with previously published work. REVIEWING Reviewing will be double blind. It will be managed by the organisers of ComAComA, assisted by the workshop???s Program Committee (see details below). INSTRUCTIONS FOR AUTHORS ** All papers will be included in the COLING workshop proceedings, in electronic form only. ** The maximum submission length is 8 pages (A4), plus two extra pages for references. ** Authors of accepted papers will be given additional space in the camera-ready version to reflect space needed for changes stemming from reviewers comments. ** The only mode of delivery will be oral; there will be no poster presentations. ** Papers shall be submitted in English, anonymised with regard to the authors and/or their institution (no author-identifying information on the title page nor anywhere in the paper), including referencing style as usual. ** Papers must conform to official COLING 2014 style guidelines, which are available on the COLING 2014 website (see also links below). ** The only accepted format for submitted papers is PDF. ** Submission and reviewing will be managed online by the START system (see link below). Submissions must be uploaded on the START system by the submission deadlines; submissions after that time will not be reviewed. To minimise network congestion, we request authors to upload their submissions as early as possible. ** In order to allow participants to be acquainted with the published papers ahead of time which in turn should facilitate discussions at the workshop, the official publication date has been set two weeks before the conference, i.e., on August 11, 2014. On that day, the papers will be available online for all participants to download, print and read. If your employer is taking steps to protect intellectual property related to your paper, please inform them about this timing. ** While submissions are anonymous, we strongly encourage authors to plan for depositing language resources and other data as well as tools used and/or developed for the experiments described in the papers, if the paper is accepted. In this respect, we encourage authors then to deposit resources and tools to available open-access repositories of language resources and/or repositories of tools (such as META-SHARE, Clarin, ELRA, LDC or AFNLP/COCOSDA for data, and github, sourceforge, CPAN and similar for software and tools), and refer to them instead of submitting them with the paper. COLING 2014 STYLE FILES Download a zip file with style files for LaTeX, MS Word and Libre Office here: http://www.coling-2014.org/doc/coling2014.zip IMPORTANT DATES May 2, 2014: Paper submission deadline June 6, 2014: Author notification deadline June 27, 2014: Camera-ready PDF deadline August 11, 2014: Official paper publication date August 23/24, 2014: ComAComA Workshop (exact date still unknown) (August 25-29, 2014: Main COLING conference) URLs Workshop: http://tinyurl.com/comacoma Main COLING conference: http://www.coling-2014.org/ Paper submission: https://www.softconf.com/coling2014/WS-17/ Style sheets: http://www.coling-2014.org/doc/coling2014.zip ORGANISING COMMITTEE Ben Verhoeven (University of Antwerp, Belgium) ben.verhoeven at uantwerpen.be Walter Daelemans (University of Antwerp, Belgium) walter.daelemans at uantwerpen.be Menno van Zaanen (Tilburg University, The Netherlands) mvzaanen at uvt.nl Gerhard van Huyssteen (North-West University, South Africa) gerhard.vanhuyssteen at nwu.ac.za PROGRAM COMMITTEE ** Preslav Nakov (University of California in Berkeley) ** Iris Hendrickx (Radboud University Nijmegen) ** Gary Libben (University of Alberta) ** Lonneke Van der Plas (University of Stuttgart) ** Helmut Schmid (Ludwig Maximilian University Munich) ** Fintan Costello (University College Dublin) ** Roald Eiselen (North-West University) ** Su Nam Kim (University of Melbourne) ** Pavol ??tekauer (P.J. Safarik University) ** Arina Banga (University of Debrecen) ** Diarmuid ?? S??aghdha (University of Cambridge) ** Rochelle Lieber (University of New Hampshire) ** Vivi Nastase (Fondazione Bruno Kessler) ** Tony Veale (University College Dublin) ** Pius ten Hacken (University of Innsbruck) ** Anneke Neijt (Radboud University Nijmegen) ** Andrea Krott (University of Birmingham) ** Emmanuel Keuleers (Ghent University) ** Stan Szpakowicz (University of Ottawa) From mpavone at dmi.unict.it Wed Mar 26 08:19:55 2014 From: mpavone at dmi.unict.it (Mario Pavone) Date: Wed, 26 Mar 2014 13:19:55 +0100 Subject: Connectionists: SSBSS 2014 News & 2nd CfP - Biology meets Engineering & Computer Science, Taormina - Sicily, Italy, June 15-19, 2014 Message-ID: <20140326131955.Horde.MRLHLuph4B9TMsXrNC5gnKA@mbox.dmi.unict.it> 2nd Call for Participation (apologies for multiple copies) ______________________________________________________ Synthetic and Systems Biology Summer School: Biology meets Engineering and Computer Science, Taormina - Sicily, Italy, June 15-19, 2014 http://www.taosciences.it/ssbss2014/ ssbss2014 at dmi.unict.it We are pleased to inform that we received more than 120 applications and 60 Abstracts/Posters and, due to many requests, we are extending the application *deadline to March 31, 2014.* For this reason, we will have up to ~150 slots (no 100 slots as previously written) for selected and motivated students. *Application Deadline: March 31, 2014* * Speakers & Courses * + Uri Alon, Weizmann Institute of Science, Israel Lecture I: Elementary Circuits in Biology Lecture II: Evolution and Optimality of Gene Circuits + Joel Bader, Johns Hopkins University, USA Lecture I: Network Remodeling during Development and Disease Lecture II: Gene and Pathway Analysis of Genome-wide Association Studies + Jef Boeke, Johns Hopkins University, USA Lecture I: Genome Synthesis Lecture II: Combinatorial DNA Assembly methods and their applications + Jason Chin, MRC Laboratory of Molecular Biology, UK Lecture I: Reprogramming the Genetic Code + Virginia Cornish, Columbia University, USA Lecture : TBA + Paul Freemont, Imperial College London, UK Lecture I: Foundational Technologies for Synthetic Biology - from DNA Assembly to Part Characterisation Lecture II: Synthetic biology designs for biosensor applications + Farren Isaacs, Yale University, USA Lecture I: Genome engineering technologies for rapid editing & evolution organisms Lecture II: Design, construction & function of genomically recoded organisms + Tanja Kortemme, University of California San Francisco, USA Lecture I: Computational protein design - principles, challenges and progress Lecture II: Design of reprogrammed and new functions - from proteins to cells + Giuseppe Nicosia, University of Catania, Italy Lecture I: Biological Circuit Design by Pareto Optimality Lecture II: Programming Living Molecular Machines for Biofuel Production + Sven Panke, ETH, Switzerland Lecture I: Synthetic Biology of Cell free Systems Lecture II: Exploiting Engineered Cell-Cell Communications in Large Scale Biotechnology + Rahul Sarpeshkar, MIT, USA Lecture I: Analog versus Digital Computation in Biology Lecture II: Analog Synthetic and Systems Biology + Giovanni Stracquadanio, Johns Hopkins University, USA Lecture I: Minimal Genomes: High-Throughput Sequencing, Statistical Methods and Physics Models to Unveil Minimal Yeast Chromosomes Compatible with Life Lecture II: Computational Tools for Genome editing, Combinatorial Assembly and Workflow Tracking + Ron Weiss, MIT, USA Lecture : TBA *School Directors* + Jef Boeke, Johns Hopkins University, USA + Giuseppe Nicosia, University of Catania, Italy + Mario Pavone, University of Catania, Italy + Giovanni Stracquadanio, Johns Hopkins University, USA *Short Talk and Poster Submission* Students may submit a research abstract for presentation. School directors will review the abstracts and will recommend for poster or short-oral presentation. Abstract should be submitted by *February 15, 2014*. The abstracts will be published on the electronic hands-out material of the summer school. Co-located Event: The 3rd International Synthetic Yeast Genome (Sc2.0) Meeting will be held in Taormina Friday June 20, 2014 http://www.taosciences.it/ssbss2014/ ssbss2014 at dmi.unict.it -- Dr. Mario Pavone (PhD) Assistant Professor Department of Mathematics and Computer Science University of Catania V.le A. Doria 6 - 95125 Catania, Italy tel: 0039 095 7383038 fax: 0039 095 330094 Email: mpavone at dmi.unict.it http://www.dmi.unict.it/mpavone/ =========================================================================== International Synthetic & Systems Biology Summer School * Biology meets Engineering and Computer Science * June 15-19, 2014 - Taormina, Italy http://www.taosciences.it/ssbss2014/ =========================================================================== 12th European Conference on Artificial Life - ECAL 2013 September 2-6, 2013 - Taormina, Italy http://mitpress.mit.edu/books/advances-artificial-life-ecal-2013 =========================================================================== From naotsu at gmail.com Wed Mar 26 10:17:43 2014 From: naotsu at gmail.com (Naotsugu Tsuchiya) Date: Thu, 27 Mar 2014 01:17:43 +1100 Subject: Connectionists: Fwd: a foundation professor in computational neuroscience at Monash, Australia In-Reply-To: References: Message-ID: We seek to appoint a dynamic and talented principal investigator to lead the development of Computational Neuroscience at Monash University. The successful candidate is expected to have an internationally recognised track record of scientific achievement and demonstrable ability to build a cutting edge multidisciplinary research platform that will transform the landscape of computational neuroscience at Monash, within Australia and internationally. This appointee will complement and integrate with experimental neuroscience researchers in the new Faculty of Biomedical and Psychological Sciences who work across a wide range of levels including single-unit recordings in non-human primates through to in vivo EEG and functional MRI in humans. These collaborations will leverage existing Monash University research platforms such as Monash Biomedical Imaging and the MASSIVE Supercomputing facility, as well as the newly established ARC Centre of Excellence for Integrative Brain Function to undertake novel, multi-disciplinary research programs. An excellent remuneration package is available for both the principal investigator and their team. Enquiries Confidential enquiries should be directed to either Professor Kim Cornish, Head of the School of Psychological Sciences, tel: +61 3 9902 0488 or email: kim.cornish at monash.edu or Professor Mark Bellgrove, Director, Research Strategy, School of Psychological Sciences, tel: +61 3 9902 4200 or email: mark.bellgrove at monash.edu Closing Date Friday, 15 May, 2014, 11:55pm Aus. Standard Daylight Time Applications Expressions of interest, comprising a cover letter and CV should be submitted as part of your application. The University reserves the right to appoint by invitation. For details, please check http://jobs.monash.edu.au/jobDetails.asp?sJobIDs=523000&lWorkTypeID=&lLocationID=&lCategoryID=641%2C+640%2C+636&lBrandID=&stp=AW&sLanguage=en -- ------------------------------------------------------------------------- Nao (Naotsugu) Tsuchiya, Ph.D. 1. ARC Future Fellow & Associate Professor Tsuchiya Laboratory School of Psychological Sciences Faculty of Medicine, Nursing and Health Sciences, Monash University 2. Japan Science and Technology Agency (JST), Japan 3. Visitor, RIKEN, Japan 4. Visitor, Caltech, USA homepage: http://users.monash.edu.au/~naotsugt/Tsuchiya_Labs_Homepage/Main.html -------------- next part -------------- An HTML attachment was scrubbed... URL: From carter at mint.is Wed Mar 26 17:26:02 2014 From: carter at mint.is (Carter Dunn) Date: Wed, 26 Mar 2014 22:26:02 +0100 Subject: Connectionists: Hiring computer vision scientist for venture backed healthcare startup Message-ID: Mint Solutions is a venture backed startup that uses computer vision to verify medication before it's given to a patient. This sharply reduces the number of medication errors in hospitals (currently 10-20% of all doses), improves patient care, and reduces costs. We are in a tight applied loop with a live hospital so you will have a direct impact on the quality of the product and user experience. Whoever takes on this role would be given full responsibility for guiding development of our computer vision pipeline in current and future versions of the product. We believe there is an incredible opportunity to develop tools to help caregivers do their jobs better and computer vision will be at their core. The current system is live in at one customer site and we have commitments from several hospitals across three countries for deployments this year. Location flexible - work from offices in the Netherlands, Iceland, your living room or a co-working space you like. Links below and please feel free to reach out with any questions. Carter http://mintsolutions.theresumator.com/apply/ZNWiog/Computer-Vision-Scientist.html http://www.mintsolutions.eu (has a video demo) t. +1 612.817.3991 | e. carter at mintsolutions.eu | s. r.carter.dunn -------------- next part -------------- An HTML attachment was scrubbed... URL: From ray.subhasis at gmail.com Thu Mar 27 05:18:52 2014 From: ray.subhasis at gmail.com (Subhasis Ray) Date: Thu, 27 Mar 2014 14:48:52 +0530 Subject: Connectionists: Computational Approaches to Memory and Plasticity: deadline approaching Message-ID: (With apologies for cross postings) Reminder for the international summer school at NCBS, Bangalore, organized by Dr. Upinder Bhalla. The deadline for application is 30 March 2014. This 15 day workshop will involve both lectures and hands-on tutorials. The confirmed speakers are: Sumantra Chattarji (NCBS, Bangalore) Xiao-Jing Wang (New York University, New York) Suhita Nadkarni (Indian Institute of Science Education and Research, Pune) Claudia Clopath (Imperial College, London) Eve Marder (Brandeis University, Waltham/Boston) Rishikesh Narayanan (Indian Institute of Science, Bangalore) Arthur Wingfield (Brandeis University, Waltham/Boston) Michael Hausser (University College, London) Arvind Kumar (Bernstein Center, Freiburg) Stefano Fusi (Columbia University, New York) Raghav Rajan (Indian Institute of Science Education and Research, Pune) Further details and application form are available here: http://ncbs.res.in/camp/ ------ Subhasis Ray NCBS, TIFR GKVK Campus Bellary Road Bangalore 560065 +91 80 67176520 -------------- next part -------------- An HTML attachment was scrubbed... URL: From christos.dimitrakakis at gmail.com Thu Mar 27 12:32:51 2014 From: christos.dimitrakakis at gmail.com (Christos Dimitrakakis) Date: Thu, 27 Mar 2014 17:32:51 +0100 Subject: Connectionists: CFP: ICML 2014 Workshop on Learning, Security and Privacy (extended) Message-ID: <533452B3.5030107@gmail.com> ICML 2014 Workshop on Learning, Security and Privacy Beijing, China, 25 or 26 June, 2014 (TBD) https://sites.google.com/site/learnsecprivacy2014/ ------------------------------------------------------------ Important Dates: - Submission deadline: 11 April, 2014 (extended!) - Notification of acceptance: 30 April, 2014 ------------------------------------------------------------ Workshop overview Many machine learning settings give rise to security and privacy requirements which are not well-addressed by traditional learning methods. Security concerns arise in intrusion detection, malware analysis, biometric authentication, spam filtering, and other applications where data may be manipulated - either at the training stage or during the system deployment - to reduce prediction accuracy. Privacy issues are common to the analysis of personal and corporate data ubiquitous in modern Internet services. Learning methods addressing security and privacy issues face an interplay of game theory, cryptography, optimization and differential privacy. Despite encouraging progress in recent years, many theoretical and practical challenges remain. Several emerging research areas, including stream mining, mobility data mining, and social network analysis, require new methodical approaches to ensure privacy and security. There is also an urgent need for methods that can quantify and enforce privacy and security guarantees for specific applications. The ever increasing abundance of data raises technical challenges to attain scalability of learning methods in security and privacy critical settings. These challenges can only be addressed in the interdisciplinary context, by pooling expertise from the traditionally disjoint fields of machine learning, security and privacy. To encourage scientific dialogue and foster cross-fertilization among these three fields, the workshop invites original submissions, ranging from ongoing research to mature work, in any of the following core subjects: - Statistical approaches for privacy preservation. - Private decision making and mechanism design. - Metrics and evaluation methods for privacy and security. - Robust learning in adversarial environments. - Learning in unknown / partially observable stochastic games. - Distributed inference and decision making for security. - Application-specific privacy preserving machine learning and decision theory. - Secure multiparty computation and cryptographic approaches for machine learning. - Cryptographic applications of machine learning and decision theory. - Security applications: Intrusion detection and response, biometric authentication, fraud detection, spam filtering, captchas. - Security analysis of learning algorithms - The economics of learning, security and privacy. Submission instructions: Submissions should be in the ICML 2014 format, with a maximum of 6 pages (including references). Work must be original. Accepted papers will be made available online at the workshop website. Submissions need not be anonymous. Submissions should be made through EasyChair: https://www.easychair.org/conferences/?conf=lps2014. For detailed submission instructions, please refer to the workshop website. Organizing committee: Christos Dimitrakakis (Chalmers University of Technology, Sweden). Pavel Laskov (University of Tuebingen, Germany). Daniel Lowd (University of Oregon, USA). Benjamin Rubinstein (University of Melbourne, Australia). Elaine Shi (University of Maryland, College Park, USA). Program committee: Asli Bay (EPFL, Switzerland) Battista Biggio (University of Cagliary, Italy) Michael Br?ckner (Amazon, Germany) Mike Burmester (Florida State University, USA) Alvaro Cardenas (University of Texas, Dallas) Kamalika Chaudhuri (UCSD, USA) Craig B Gentry (IBM Research, USA) Alex Kantchelian (UC Berkeley, USA) Aikaterini Mitrokotsa (Chalmers University, Sweden) Blaine Nelson (University of Potsdam, Germany) Norman Poh (University of Surrey, UK) Konrad Rieck (University of G?ttingen) Nedim Srndic (University of Tuebingen, Germany) Aaron Roth (University of Pennsylvania, USA) Risto Vaarandi (NATO CCDCOE, Estonia) Sobha Venkataraman (AT&T Research, USA) Ting-Fang Yen (EMC, USA) -- Christos Dimitrakakis http://www.cse.chalmers.se/~chrdimi/ From axel.hutt at inria.fr Fri Mar 28 09:35:37 2014 From: axel.hutt at inria.fr (Axel Hutt) Date: Fri, 28 Mar 2014 14:35:37 +0100 (CET) Subject: Connectionists: Reminder: Research topic on General Anaesthesia In-Reply-To: <1622840021.678379.1396013729368.JavaMail.zimbra@inria.fr> Message-ID: <18538477.678471.1396013737753.JavaMail.zimbra@inria.fr> Reminder -------- Research Topic in Frontiers in Systems Neuroscience on General anesthesia: from theory to experiments General anesthesia is a standard medical procedure in todays' hospital practice. This Research Topic aims to address recent theoretical and experimental advances in the field. Each article in the issue focusses on a specific current research topic in general anesthesia research and introduces to the topic in a pedagogical way. The issue attempts to cover various aspects of anaesthesia and the most important topics in the field, such as (but not limited to) recent advances in theoretical models of fMRI/EEG/MEG/LFP data, states of consciousness reflected in fMRI/EEG/LFP data, relation of anaesthesia and sleep, the connectivity changes during anesthesia observed by fMRI/EEG or effects of specific drugs on brain activity. Topic Editors: Axel Hutt, INRIA CR Nancy, France Anthony G. Hudetz, Medical College of Wisconsin, USA Deadline for abstract submission: 30 Apr 2014 Deadline for full article submission: 30 Jun 2014 More information under http://www.frontiersin.org/systems_neuroscience/researchtopics/general_anesthesia_from_theory/2345 -- Dr. rer. nat. Axel Hutt, HDR INRIA CR Nancy - Grand Est Equipe NEUROSYS (Head) 615, rue du Jardin Botanique 54603 Villers-les-Nancy Cedex France http://www.loria.fr/~huttaxel From weng at cse.msu.edu Fri Mar 28 19:44:02 2014 From: weng at cse.msu.edu (Juyang Weng) Date: Fri, 28 Mar 2014 19:44:02 -0400 Subject: Connectionists: Brain-Mind Institute 2014 Program: Offer Courses and International Conference on Brain-Mind (ICBM) In-Reply-To: <533457F5.6050800@cse.msu.edu> References: <533457F5.6050800@cse.msu.edu> Message-ID: <53360942.40301@cse.msu.edu> Dear colleagues: With the great support from many colleagues, the Brain-Mind Institute (BMI) has successfully run for two years, with distinguished talks, summer schools, International Conference on Brain-Mind (ICBM), and the Brain-Mind Magazine (BMM). Its 2014 summer school and ICBM will run in Beijing, China, collocating with WCCI 2014 . Brain-Mind Institute (BMI) http://www.brain-mind-institute.org/ Preliminary Program: Summer 2014 Summer School, Beijing, June 16 - July 4, 2014 (weeks 1-3) (WCCI 2014, Beijing, not a part of BMI Program, but BMI participants are encouraged to participate) (week 4: Saturday - Friday) International Conference on Brain-Mind (ICBM), Beijing, July 12 - 13, 2014 (week 4: weekend) Summer School, Beijing, July 14 - August 1, 2014 (weeks 5-7) Co-Sponsored and Co-Organized by Brain-Mind Institute and Brainnetome Center of CASIA, Beijing, China BMI Courses: Session 1: BMI 831 Cognitive Science for Brain-Mind Research. 3 credits. On-site at CASIA or distance learning. BMI 831 certificate is issued for those who pass the course. Session 2: BMI 871 Computational Brain-Mind. 3 credits. On-site at CASIA or distance learning. BMI 871 certificate is issued for those who pass the course. Full-time students can apply for tuition waivers. ICBM: The subjects of interest include, but not limited to: 1. *Genes*: inheritance, evolution, species, environments, nature vs. nurture, and evolution vs. development. 2. *Cells*: cell models, cell learning, cell signaling, tissues, morphogenesis, and tissue development. 3. *Circuits*: features, clustering, self-organization, cortical circuits, Brodmann areas, representation, classification, and regression. 4. *Streams*: pathways, intra-modal attention, vision, audition, touch (including kinesthetics, temperature), smell, and taste. 5. *Brain ways*: neural networks, brain-mind architecture, inter-modal attention, multisensory integration, and neural modulation (punishment/serotonin/pain, reward/dopamine/pleasure/sex, novelty/acetylcholine/norepinephrine, higher emotion). 6. *Experiences/learning*: training, learning, development, interaction, performance metrics, and functions of genome. 7. *Behaviors:* actions, motor development, concept learning, abstraction, languages, decision making, reasoning, and creativity. 8. *Societies/multi-agent*: joint attention, swarm intelligence, group intelligence, genders, races, science of organization, constitutions, laws, and cultures. 9. *Diseases*: depression, ADD/ADHD, drug addiction, dyslexia, autism, schizophrenia, Alzheimer's disease, Parkinson's disease, vision loss, and hearing loss. 10. *Applications*: image analysis, computer vision, speech recognition, pattern recognition, robotics, artificial intelligence, instrumentation, and prosthetics. *Important dates: *Course applications (to get admitted so that you can register): by Sunday, April 6, 2014 Tuition waiver applications (full-time students only, mark in your course application): by Sunday, April 6, 2014 ICBM full papers: by Sunday, April 13, 2014 ICBM abstracts: by Sunday, April 20, 2014 Notification of admission: April 27, 2014 Notification of tuition waiver: April 27, 2014 Advance registration: by Sunday, May 4, 2014 ---------------------------------------------- -------------- next part -------------- An HTML attachment was scrubbed... URL: From grlmc at urv.cat Sat Mar 29 10:14:47 2014 From: grlmc at urv.cat (GRLMC) Date: Sat, 29 Mar 2014 15:14:47 +0100 Subject: Connectionists: SSTiC 2014: April 12, 5th registration deadline Message-ID: <3089489E29E2498FA846BBB36A509C66@Carlos1> *To be removed from our mailing list, please respond to this message with UNSUBSCRIBE in the subject line* ********************************************************************* 2014 TARRAGONA INTERNATIONAL SUMMER SCHOOL ON TRENDS IN COMPUTING SSTiC 2014 Tarragona, Spain July 7-11, 2014 Organized by Rovira i Virgili University http://grammars.grlmc.com/sstic2014/ ********************************************************************* --- April 12, 5th registration deadline --- ********************************************************************* AIM: SSTiC 2014 is the second edition in a series started in 2013. For the previous event, see http://grammars.grlmc.com/SSTiC2013/ SSTiC 2014 will be a research training event mainly addressed to PhD students and PhD holders in the first steps of their academic career. It intends to update them about the most recent developments in the diverse branches of computer science and its neighbouring areas. To that purpose, renowned scholars will lecture and will be available for interaction with the audience. SSTiC 2014 will cover the whole spectrum of computer science through 6 keynote lectures and 24 six-hour courses dealing with some of the most lively topics in the field. The organizers share the idea that outstanding speakers will really attract the brightest students. ADDRESSED TO: Graduate students from around the world. There are no formal pre-requisites in terms of the academic degree the attendee must hold. However, since there will be several levels among the courses, reference may be made to specific knowledge background in the description of some of them. SSTiC 2014 is also appropriate for more senior people who want to keep themselves updated on developments in their own field or in other branches of computer science. They will surely find it fruitful to listen and discuss with scholars who are main references in computing nowadays. REGIME: In addition to keynotes, 3 parallel sessions will be held during the whole event. Participants will be able to freely choose the courses they will be willing to attend as well as to move from one to another. VENUE: SSTiC 2014 will take place in Tarragona, located 90 kms. to the south of Barcelona. The venue will be: Campus Catalunya Universitat Rovira i Virgili Av. Catalunya, 35 43002 Tarragona KEYNOTE SPEAKERS: Larry S. Davis (U Maryland, College Park), A Historical Perspective of Computer Vision Models for Object Recognition and Scene Analysis David S. Johnson (Columbia U, New York), Open and Closed Problems in NP-Completeness George Karypis (U Minnesota, Twin Cities), Recommender Systems Past, Present, & Future Steffen Staab (U Koblenz), Explicit and Implicit Semantics: Two Sides of One Coin Philip Wadler (U Edinburgh), You and Your Research and The Elements of Style Ronald R. Yager (Iona C, New Rochelle), Social Modeling COURSES AND PROFESSORS: Divyakant Agrawal (U California, Santa Barbara), [intermediate] Scalable Data Management in Enterprise and Cloud Computing Infrastructures Pierre Baldi (U California, Irvine), [intermediate] Big Data Informatics Challenges and Opportunities in the Life Sciences Rajkumar Buyya (U Melbourne), [intermediate] Cloud Computing John M. Carroll (Pennsylvania State U, University Park), [introductory] Usability Engineering and Scenario-based Design Kwang-Ting (Tim) Cheng (U California, Santa Barbara), [introductory/intermediate] Smartphones: Hardware Platform, Software Development, and Emerging Apps Amr El Abbadi (U California, Santa Barbara), [introductory] The Distributed Foundations of Data Management in the Cloud Richard M. Fujimoto (Georgia Tech, Atlanta), [introductory] Parallel and Distributed Simulation Mark Guzdial (Georgia Tech, Atlanta), [introductory] Computing Education Research: What We Know about Learning and Teaching Computer Science David S. Johnson (Columbia U, New York), [introductory] The Traveling Salesman Problem in Theory and Practice George Karypis (U Minnesota, Twin Cities), [intermediate] Programming Models/Frameworks for Parallel & Distributed Computing Aggelos K. Katsaggelos (Northwestern U, Evanston), [intermediate] Optimization Techniques for Sparse/Low-rank Recovery Problems in Image Processing and Machine Learning Arie E. Kaufman (U Stony Brook), [advanced] Visualization Carl Lagoze (U Michigan, Ann Arbor), [introductory] Curation of Big Data Dinesh Manocha (U North Carolina, Chapel Hill), [introductory/intermediate] Robot Motion Planning Bijan Parsia (U Manchester), [introductory] The Empirical Mindset in Computer Science Charles E. Perkins (FutureWei Technologies, Santa Clara), [intermediate] Beyond LTE: the Evolution of 4G Networks and the Need for Higher Performance Handover System Designs Sudhakar M. Reddy (U Iowa, Iowa City), [introductory] Test and Design for Test of Digital Logic Circuits Robert Sargent (Syracuse U), [introductory] Validation of Models Mubarak Shah (U Central Florida, Orlando), [intermediate] Visual Crowd Analysis Steffen Staab (U Koblenz), [intermediate] Programming the Semantic Web Mike Thelwall (U Wolverhampton), [introductory] Sentiment Strength Detection for Twitter and the Social Web Jeffrey D. Ullman (Stanford U), [introductory] MapReduce Algorithms Nitin Vaidya (U Illinois, Urbana-Champaign), [introductory/intermediate] Distributed Consensus: Theory and Applications Philip Wadler (U Edinburgh), [intermediate] Topics in Lambda Calculus and Life ORGANIZING COMMITTEE: Adrian Horia Dediu (Tarragona) Carlos Mart?n-Vide (Tarragona, chair) Florentina Lilica Voicu (Tarragona) REGISTRATION: It has to be done at http://grammars.grlmc.com/sstic2014/registration.php The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an approximation of the respective demand for each course. Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed when the capacity of the venue will be complete. It is very convenient to register prior to the event. FEES: As far as possible, participants are expected to attend for the whole (or most of the) week (full-time). Fees are a flat rate allowing one to participate to all courses. They vary depending on the registration deadline. ACCOMMODATION: Information about accommodation is available on the website of the School. CERTIFICATE: Participants will be delivered a certificate of attendance. QUESTIONS AND FURTHER INFORMATION: florentinalilica.voicu at urv.cat POSTAL ADDRESS: SSTiC 2014 Lilica Voicu Rovira i Virgili University Av. Catalunya, 35 43002 Tarragona, Spain Phone: +34 977 559 543 Fax: +34 977 558 386 ACKNOWLEDGEMENTS: Departament d?Economia i Coneixement, Generalitat de Catalunya Universitat Rovira i Virgili From ray.subhasis at gmail.com Sun Mar 30 06:39:53 2014 From: ray.subhasis at gmail.com (Subhasis Ray) Date: Sun, 30 Mar 2014 16:09:53 +0530 Subject: Connectionists: Application deadline extended till Apr 14 for international summer school: CAMP@Bangalore Message-ID: (with apology for cross positing) The deadline for applying to the international summer school: Computational Approaches to Memory and Plasticity, CAMP at Bangalore 2014is extended to 14th April, 2014. Please download the poster and circulate widely. Details follow ... Computational Approaches to Memory and Plasticity (CAMP @ Bangalore) 2014 . Website: http://www.ncbs.res.in/camp/ Venue: National Centre for Biological Sciences, Bangalore, India Dates: 28th June to 12th July, 2014 Organizer: Upinder Bhalla (NCBS, Bangalore, India) CAMP @ Bangalore is a 15-day summer school on the theory and simulation of learning, memory and plasticity in the brain. PhD students and post-docs from theoretical and/or experimental backgrounds (physics, math, neuroscience, engineering, etc.) are welcome. Familiarity with programming, dynamical systems, and/or computational neuroscience is highly desirable. Master's or Bachelor's degree students can apply if they have sufficient background beyond course-work. The school will start with remedial tutorials on neuroscience / math / programming and then work upwards from sub-cellular electrical and chemical signaling in neurons, onward to micro-circuits and networks, all with an emphasis on learning, memory and plasticity. Students worldwide are encouraged to apply online at www.ncbs.res.in/camp latest by 14th Apr, 2014. Lecturers: Sumantra Chattarji (NCBS, Bangalore) , Xiao-Jing Wang (New York University, New York) , Suhita Nadkarni (Indian Institute of Science Education and Research, Pune) , Claudia Clopath (Imperial College, London) , Eve Marder (Brandeis University, Waltham/Boston) , Rishikesh Narayanan (Indian Institute of Science, Bangalore) , Arthur Wingfield (Brandeis University, Waltham/Boston) , Michael Hausser (University College, London) , Arvind Kumar (Bernstein Center, Freiburg) , Stefano Fusi (Columbia University, New York) , Raghav Rajan (Indian Institute of Science Education and Research, Pune) For more information, email camp2014 at ncbs.res.in ---- Subhasis Ray National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK, Bellary Road, Bangalore - 560065, India. Ph: 91-80-23666520. -------------- next part -------------- An HTML attachment was scrubbed... URL: From belardinelli at informatik.uni-tuebingen.de Sun Mar 30 10:51:47 2014 From: belardinelli at informatik.uni-tuebingen.de (Anna Belardinelli) Date: Sun, 30 Mar 2014 16:51:47 +0200 Subject: Connectionists: KOGWIS 2014 Call for Papers Message-ID: Please consider to submit your work to the following conference: ================ FINAL CALL FOR PAPERS ================ * KOGWIS 2014 * * 12th Biannual Conference of the German Society for COGNITIVE SCIENCE * * 29th of September - 2nd of October 2014 * * Topical focus: How Language and Behavior Constitute Cognition * Submission deadline: 7 May 2014 * * http://www.ccs.uni-tuebingen.de/kogwis14 * ====================================================== KogWis 2014 invites submissions of extended abstracts on current work in cognitive science. Generally *all topics related to cognitive science* are welcome. Contributions that address the focus of this meeting, that is, language and behavior and the construction of cognition due to language and behavior are particularly encouraged. Submissions will be sorted by topic and paradigms and will be independently reviewed. Notifications of acceptance will depend on the novelty of the research, the significance of the results, and the presentation of the work. Submission and Proceedings: KogWis calls for submissions in extended abstract format. Abstracts for oral presentations should not exceed 4 pages, abstracts for poster contributions should not exceed 2 pages, including pictures and references. All accepted contributions will be published in the form of a *Supplement KogWis 2014* in the journal *Cognitive Processing* (Springer Verlag). ??? PROGRAM HIGHLIGHTS ?????? Confirmed plenary speakers: * Prof. Dr. Harold Bekkering * Prof. Dr. Henrik Ehrsson * Prof. Dr. Karl Friston * Prof. Dr. Wayne Gray * Prof. Dr. Gregor Sch?ner * Prof. Dr. Natalie Sebanz Confirmed symposia: * Processing Language in Context: Insights from Empirical Approaches Organizers: Christian Brauner , Bettina Rolke, & Gerhard J?ger (University of T?bingen) * Manual action Organizer: Dirk Koester (Bielefeld University) * How language and number representation contribute to numerical cognition Organizer: Hans-Christoph Nuerk (University of T?bingen) * Eye tracking, linking hypotheses and measures in language processing Organizers: Pia Knoeferle and Michele Burigo (Bielefeld University) * Cognition of human actions: From individual actions to interactions Organizer: Stephan de la Rosa (MPI, T?bingen) * Cortical Systems of Object Grasping and Manipulation Organizer: Marc Himmelbach (University of T?bingen) * Modelling of cognitive aspects of mobile interaction Organizers: Nele Russwinkel, Sabine Prezenski, & Stefan Lindner (TU Berlin) * Predictive processing: philosophical and neuroscientific perspectives Organizer: Alex Morgan (University of T?bingen) * PhD Symposium Organizers: Dong-Seon Chang, Zhen Peng, Stefan Kopp, Marco Ragni, and Kai Vogeley (MPI T?bingen, Bielefeld University, University of Freiburg, & University Hospital Cologne) * History of Cognitive Science Organizers: Stefan Kopp and Kai Vogeley (Bielefeld University and University Hospital Cologne) ****** PRIZES AND AWARDS****** * CCS - Best Student Paper Award, sponsored by the Cognitive Science Society (plus 1 year Membership) * SMI - Best Poster Award, sponsored by SensoMotoric Instruments (SMI,www.smivision.com) * Brain Products - Best Paper Award, sponsored by BRAIN PRODUCTS _____________ The Organizing Committee: General chair: Martin V. Butz Local organizers: Anna Belardinelli, Elisabeth Hein and Jan Kneissler ------------------------------------------- Anna Belardinelli, Cognitive Modeling, Department of Computer Science University of T?bingen http://www.wsi.uni-tuebingen.de/lehrstuehle/cognitive-modeling/staff/staff/anna-belardinelli.html -------------- next part -------------- An HTML attachment was scrubbed... URL: From basab at ieee.org Mon Mar 31 06:23:02 2014 From: basab at ieee.org (Basabdatta Sen Bhattacharya) Date: Mon, 31 Mar 2014 11:23:02 +0100 Subject: Connectionists: PhD position available at the School of Engineering, University of Lincoln Message-ID: A fully funded PhD is available at the School of Engineering, University of Lincoln, United Kingdom, to work under the supervision of Dr. Basabdatta Bhattacharya and Prof. Timothy Gordon . The details and how to apply are available at: https://sites.google.com/site/bsenbhattacharya/phd/fully-fundedphdstudentships The *Deadline* for Application is *18th April 2014.* For informal enquiries please contact Basabdatta Sen Bhattacharya at bbhattacharya at lincoln.ac.uk or at basab at ieee.org. A link to the advert on the University website is available at the link below; scroll down to the heading 'College of Science': http://www.lincoln.ac.uk/home/studyatlincoln/postgraduateprogrammes/postgraduateresearch/studentships/ *Note:* The *full funding* is for UK/EU students *only*. International students would need to provide the difference in fees as appropriate. -- --------------------------------------------------------------- Basabdatta Sen Bhattacharya, PhD Lecturer, School of Engineering ENG 321, Engineering Hub University of Lincoln Brayford Pool, Lincoln LN6 7TS United Kingdom Telephone: +44(0)1522837947 *http://staff.lincoln.ac.uk/bbhattacharya * Personal page: https://sites.google.com/site/bsenbhattacharya -------------- next part -------------- An HTML attachment was scrubbed... URL: From callforvideos at aaaivideos.org Mon Mar 31 08:45:35 2014 From: callforvideos at aaaivideos.org (AAAI Video Competition) Date: Mon, 31 Mar 2014 14:45:35 +0200 Subject: Connectionists: Reminder: Call for Videos: AAAI-14 AI Video Competition Message-ID: AAAI-14 AI Video Competition Date: July 28, 2014 Place: Qu?bec City, Qu?bec, Canada Website: http://www.aaaivideos.org Video: http://youtu.be/uwD5qN-MF5M Submission Deadline: April 15, 2014 ------- Dear Colleagues, AAAI is pleased to announce the continuation of the AAAI Video Competition, now entering its eighth year. The video competition will be held in conjunction with the AAAI-14 conference in Qu?bec City, Qu?bec, Canada, July 27-31, 2014. At the award ceremony, authors of award-winning videos will be presented with "Shakeys", trophies named in honour of SRI's Shakey robot and its pioneering video. Award-winning videos will be screened at this ceremony. The goal of the competition is to show the world how much fun AI is by documenting exciting artificial intelligence advances in research, education, and application. View previous entries and award winners at http://www.aaaivideos.org/past_competitions. The rules are simple: Compose a short video about an exciting AI project, and narrate it so that it is accessible to a broad online audience. We strongly encourage student participation. To increase reach, select videos will be uploaded to Youtube and promoted through social media (twitter, facebook, g+) and major blogs in AI and robotics. VIDEO FORMAT AND CONTENT Either a 1 minute (max) short video or a 5 minute (max) long video, with English narration (or English subtitles). Consider combining screenshots, interviews, and videos of a system in action. Make the video self-contained, so that newcomers to AI can understand and learn from it. We encourage a good sense of humor, but will only accept submissions with serious AI content. For example, we welcome submissions of videos that: * Highlight a research topic - contemporary or historic, your own or from another group * Introduce viewers to an exciting new AI-related technology * Provide a window into the research activities of a laboratory and/or senior researcher * Attract prospective students to the field of AI * Explain AI concepts - your video could be used in the classroom Please note that this list is not exhaustive. Novel ideas for AI-based videos, including those not necessarily based on a "system in action", are encouraged. No matter what your choice is, creativity is encouraged! (Please note: The authors of previous, award-winning videos typically used humor, background music, and carefully selected movie clips to make their contributions come alive.) Please also note that videos should only contain material for which the authors own copyright. Clips from films or television and music for the soundtrack should only be used if copyright permission has been granted by the copyright holders, and this written permission accompanies the video submission. SUBMISSION INSTRUCTIONS Submit your video by making it available for download on a (preferably password-protected) dropbox, ftp or website. Once you have done so, please fill out the submission form (http://www.aaaivideos.org/submission_form.txt) and send it to us by email (submission at aaaivideos.org). All submissions are due no later than April 15, 2014. REVIEW AND AWARD PROCESS Submitted videos will be peer-reviewed by members of the programme committee according to the criteria below. Videos that receive positive reviews will be accepted for publication in the AAAI Video Competition proceedings. Select videos will also be uploaded to Youtube, promoted through social media, and featured on the dedicated website ( http://www.aaaivideos.org). The best videos will be nominated for awards. Winners will be revealed at the award ceremony during AAAI-14. All authors of accepted videos will be asked to sign a distribution license form. Review criteria: 1. Relevance to AI (research or application) 2. Excitement generated by the technology presented 3. Educational content 4. Entertainment value 5. Presentation (cinematography, narration, soundtrack, production values) AWARD CATEGORIES Best Video, Best Short Video, Best Student Video, Most Jaw-Dropping Technology, Most Educational, Most Entertaining and Best Presentation. (Categories may be changed at the discretion of the chairs.) AWARDS Trophies ("Shakeys"). KEY DATES * Submission Deadline: April 15, 2014 * Reviewing Decision Notifications & Award Nominations: May 15, 2014 * Final Version Due: June 15, 2014 * Screening and Award Presentations: July 28, 2014 FOR MORE INFORMATION Please contact us at info at aaaivideos.org We look forward to your participation in this exciting event! Mauro Birattari and Sabine Hauert AAAI Video Competition 2014 -------------- next part -------------- An HTML attachment was scrubbed... URL: From marcel.van.gerven at gmail.com Mon Mar 31 13:40:12 2014 From: marcel.van.gerven at gmail.com (Marcel van Gerven) Date: Mon, 31 Mar 2014 19:40:12 +0200 Subject: Connectionists: Donders Summer School on Neural Metrics Message-ID: <718E0C75-4312-4C1A-8500-182209ACDEC5@gmail.com> Radboud Summer School Neural metrics: Quantitative analysis of neural organisation and function The Donders Institute for Brain, Cognition and Behaviour is organizing a summer school on neural metrics where the aim is to get participants acquainted with the quantitative analysis of neural organisation and function. We have compiled an exciting program with an excellent set of national and international speakers. Topics range from the analysis of single-neuron responses to the analysis of macroscopic brain networks. The course is designed for PhD students and starting postdoctoral researchers working at the interface between cognitive neuroscience and the application of advanced methods. Please consult the Radboud Summer School website for details on the programme, social events and registration. Further details for the Neural Metrics Summer School can be found below and on the website. Note that the early bird deadline is April 1st. Dates Monday 11 August - Friday 15 August 2014 (one week) Course leaders Dr. M. (Marcel) van Gerven, Assisstant Professor Prof. T. (Tansu) Celikel, Professor Neurophysiology Donders Institute for Brain, Cognition and Behaviour Entry level PhD students in the field of Neuroscience with an MSc in Biology, Computer Science, Psychology, Physics, Al or similar subject For whom is this course designed This course was developed for PhD students (10 local + 20 international) and early postdoctoral researchers working at the interface between cognitive neuroscience and the application of advanced methods. This includes cognitive neuroscientists and researchers with practical experience. Admission requirements As part of the admission procedure, we ask you to send us your CV and a motivation letter in which you explain your interest for our course. Course fee ?400 The course fee includes the registration fee, course materials, access to library and IT facilities, coffee/tea, lunch, and a number of social activities. Accommodation is available for the course participants. For details please see http://www.ru.nl/radboudsummerschool/practical-matters/housing/ Discounts ? 10% discount for early bird applicants. The early bird deadline is 1 April 2014. ? 15% discount for students and PhD candidates from Radboud University and partner universities Course description The brain as a network In order to fully understand the neuronal substrate of human cognition, we need to start viewing the working brain as a network.Many neurological and mental disorders are viewed as the result of a malfunction at the network level. The growing interest in brain networks is complemented by rapid theoretical and technical developments. We will embrace these developments throughout the course to help us understand the human brain network. This will be done by examining the theoretical background and by learning the necessary measurement and data analysis techniques used to study brain connectivity. The topics will be structured according to micro, meso and macro-scale connectivity. Course contents Micro-scale connectivity involves neuron communication at the cellular and synaptic level; mesa-scale connectivity addresses communication between brain regions; and macro-scale connectivity explores the structure and dynamics of large brain networks. We will strive to include components of electrophysiology, anatomy, functional blood flow measures, computational modelling and advanced data analysis at each level. While doing so, we will focus on both the animal and human brain. The course consists of lectures and computer exercises, supplemented with in-depth discussions. Lecture slides and exercises will also be distributed among the participants. We will ensure active participation in the following way: ? In the hands-on sessions you will work together in small groups (2-3 people). These sessions will include exercises aimed at promoting discussions and encouraging you to reflect on the core theoretical issues. Learning outcomes 1. Understand new techniques and approaches in the field of neuroscience networks 2. Understand the basics of new connectivity analysis tools 3. Understand new theories and computational modelling approaches 4. Identify the appropriate research methodology for answering specific research questions on brain connectivity 5. Improve your communication skills and develop research questions in a group setting Number of ECTS credits 2 ECTS credits Registration For registration, please consult the website. More information radboudsummerschool at ru.nl -------------- next part -------------- An HTML attachment was scrubbed... URL: From ted.carnevale at yale.edu Mon Mar 31 14:01:03 2014 From: ted.carnevale at yale.edu (Ted Carnevale) Date: Mon, 31 Mar 2014 14:01:03 -0400 Subject: Connectionists: 2014 NEURON Summer Course Message-ID: <5339AD5F.1000308@yale.edu> This year's NEURON Summer Course has two components that can be taken individually or together. NEURON Fundamentals, which runs from June 21-24, addresses all aspects of using NEURON to model individual neurons, and also introduces parallel simulation and the fundamentals of modeling networks. Parallel Simulation with NEURON is for users who are already familiar with NEURON and need to create models that will run on parallel hardware. Registration is limited, and the registration deadline is Friday, May 30, 2014. For more information regarding NEURON courses see http://www.neuron.yale.edu/neuron/courses --Ted From gunnar.blohm at gmail.com Mon Mar 31 15:14:03 2014 From: gunnar.blohm at gmail.com (Gunnar Blohm) Date: Mon, 31 Mar 2014 15:14:03 -0400 Subject: Connectionists: Summer school in Computational Sensory-Motor Neuroscience (CoSMo 2014) Message-ID: <5339BE7B.1010401@queensu.ca> *Fourth Annual Computational Sensory-Motor Neuroscience Summer School (CoSMo 2014)* University of Minnesota, Minneapolis, MN, USA August 3-17, 2014 We would like to invite you to join us for the third annual Computational Sensory-Motor Neuroscience Summer School. The course is about experimental, computational and medical aspects of sensory-motor neuroscience with a focus on data/model sharing. Covered topics include Bayesian approaches, motor control, computational neuroimaging, sensory-motor transformations and prosthetics. An important focus is on doing research as opposed to just hearing about it. Each teaching module will take up two days with morning lecture sessions. Afternoon sessions involve hands-on Matlab programming, simulation and data-analysis. Newly acquired computational tools can also be applied in 2-week evening group research projects. The course is aimed at students and post-doctoral fellows from diverse backgrounds including Life Sciences, Psychology, Computer Science, Physics, Mathematics and Engineering. Basic knowledge in calculus, linear algebra and Matlab is expected. Enrollment will be limited to 40 trainees. *Application deadline: April 27, 2014* For more information and to apply, please go to http://www.compneurosci.com/CoSMo/ The school is co-organized by Drs Gunnar Blohm, Paul Schrater and Konrad K?rding. It receives funding from the National Sciences and Engineering Research Council of Canada (NSERC) via an NSERC-CREATE training grant on "Computational Approaches in Neuroscience ? Action, Control & Transformations", and from the National Science Foundation (NSF, USA). -- ------------------------------------------------------- Dr. Gunnar BLOHM Assistant Professor in Computational Neuroscience Centre for Neuroscience Studies, Departments of Biomedical and Molecular Sciences, Mathematics & Statistics, and Psychology, School of Computing, and Canadian Action and Perception Network (CAPnet) Queen?s University 18, Stuart Street Kingston, Ontario, Canada, K7L 3N6 Tel: (613) 533-3385 Fax: (613) 533-6840 Email:Gunnar.Blohm at QueensU.ca Web:http://www.compneurosci.com/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From ralph.etiennecummings at gmail.com Mon Mar 31 20:59:53 2014 From: ralph.etiennecummings at gmail.com (Ralph Etienne-Cummings) Date: Mon, 31 Mar 2014 20:59:53 -0400 Subject: Connectionists: Deadline is April 2nd (but site will be open until April 4th): Call for Applications in 2014 Telluride Neuromorphic Cognition Engineering Workshop Message-ID: Telluride Neuromorphic Cognition Engineering Workshop 2014 Neuromorphic Cognition Engineering Workshop: The 20th Anniversary Edition Telluride, Colorado, June 29th - July 19th, 2014 CALL FOR APPLICATIONS: Deadline is April 2nd, 2014 NEUROMORPHIC COGNITION ENGINEERING WORKSHOP www.ine-web.org Sunday June 29th - Saturday July 19th, 2014, Telluride, Colorado We invite applications for a three-week summer workshop that will be held in Telluride, Colorado.Sunday June 29th - Saturday July 19th, 2014. The application deadline is Wednesday, April 2nd and application instructions are described at the bottom of this document. This is the 20th Anniversary of the Workshop, and ~25 years since the conception of the "Meadian" version of Neuromorphic Engineering. Hence, we plan a celebratory Workshop, where some of the originators and benefactors of the field will participate in discussions of the successes and challenges over the past 25 years and prognosticate the potential contributions for the next 25 years. The 2014 Workshop and Summer School on Neuromorphic Engineering is sponsored by the National Science Foundation, Institute of Neuromorphic Engineering, Qualcomm Corporation, The EU-Collaborative Convergent Science Network (CNS-II), University of Maryland - College Park, Institute for Neuroinformatics - University of Zurich and ETH Zurich, Georgia Institute of Technology, Johns Hopkins University, Boston University, University of Western Sydney and the Salk Institute. Directors: Cornelia Fermuller, University of Maryland, College Park Ralph Etienne-Cummings, Johns Hopkins University Shih-Chii Liu, Institute of Neuroinformatics, UNI/ETH Zurich, Switzerland Timothy Horiuchi, University of Maryland, College Park Workshop Advisory Board: Andreas Andreou, Johns Hopkins University Andre van Schaik, University Western Sydney, Australia Avis Cohen, University of Maryland Barbara Shinn-Cunningham, Boston University Giacomo Indiveri, Institute of Neuroinformatics, Uni/Eth Zurich, Switzerland Jonathan Tapson, University Western Sydney, Australia Malcolm Slaney, Microsoft Research Jennifer Hasler, Georgia Institute of Technology Rodney Douglas, Institute of Neuroinformatics, Uni/Eth Zurich, Switzerland Shihab Shamma, University of Maryland Tobi Delbruck, Institute of Neuroinformatics, Uni/Eth Zurich, Switzerland Previous year workshop can be found at: ine-web.org/workshops/workshops-overview/index.htmland the workshop wiki is athttps://neuromorphs.net/ GOALS: Neuromorphic engineers design and fabricate artificial neural systems whose organizing principles are based on those of biological nervous systems. Over the past 18 years, this research community has focused on the understanding of low-level sensory processing and systems infrastructure; efforts are now expanding to apply this knowledge and infrastructure to addressing higher-level problems in perception, cognition, and learning. In this 3-week intensive workshop and through the Institute for Neuromorphic Engineering (INE), the mission is to promote interaction between senior and junior researchers; to educate new members of the community; to introduce new enabling fields and applications to the community; to promote on-going collaborative activities emerging from the Workshop, and to promote a self-sustaining research field. FORMAT: The three week summer workshop will include background lectures on systems and cognitive neuroscience (in particular sensory processing, learning and memory, motor systems and attention), practical tutorials on emerging hardware design, mobile robots, hands-on projects, and special interest groups. Participants are required to take part and possibly complete at least one of the projects proposed. They are furthermore encouraged to become involved in as many of the other activities proposed as interest and time allow. There will be two lectures in the morning that cover issues that are important to the community in general. Because of the diverse range of backgrounds among the participants, some of these lectures will be tutorials, rather than detailed reports of current research. These lectures will be given by invited speakers. Projects and interest groups meet in the late afternoons, and after dinner. In the early afternoon there will be tutorials on a wide spectrum of topics, including analog VLSI, mobile robotics, vision and auditory systems, central-pattern-generators, selective attention mechanisms, cognitive systems, etc. 2014 TOPIC AREAS: Human Auditory Cognition: Acoustic Priming, Imagination and Attention. Project Leaders: Shihab Shamma (UM-College Park), Malcolm Slaney (Microsoft), Edward Lalor (Trinity College, Dublin), Barbara Shinn-Cunningham (Boston U) Motion and Action Processing on Wearable Devices Project Leaders: Michael Pfeiffer (INI-UZH), Ryad Benosman (UPMC, Paris), Garrick Orchard (NUS, Singapore), and Cornelia Ferm?ller (UMCP) Planning with Dynamic Neural Fields: from Sensorimotor Dynamics to Large-Scale behavioral Search Project Leaders: Yulia Sandamirskaya (RUB, Bochum) and Erik Billing (U. Skovde) Neuromorphic Olympics Project Leaders: Jorg Conradt (TUM, Munich) and Terry Stewart (U. Waterloo) Embodied Neuromorphic Real-World Architectures of Perception, Cognition and Action Project Leaders: Andreas Andreou (JHU) and Paul Verschure (UPF, Barcelona) Terry Sejnowski (Salk Institute) - Computational Neuroscience (invitational mini-workshop) LOCATION AND ARRANGEMENTS: The summer school will take place in the small town of Telluride, 9000 feet high in southwest Colorado, about 6 hours drive away from Denver (350 miles). Great Lakes Aviation and America West Express airlines provide daily flights directly into Telluride. All facilities within the beautifully renovated public school building are fully accessible to participants with disabilities. Participants will be housed in ski condominiums, within walking distance of the school. Participants are expected to share condominiums. The workshop is intended to be very informal and hands-on. Participants are not required to have had previous experience in analog VLSI circuit design, computational or machine vision, systems level neurophysiology or modeling the brain at the systems level. However, we strongly encourage active researchers with relevant backgrounds from academia, industry and national laboratories to apply, in particular if they are prepared to work on specific projects, talk about their own work or bring demonstrations to Telluride (e.g. robots, chips, software). Wireless internet access will be provided. Technical staff present throughout the workshops will assist with software and hardware issues. We will have a network of PCs running LINUX and Microsoft Windows for the workshop projects. We encourage participants to bring along their personal laptop. No cars are required. Given the small size of the town, we recommend that you do not rent a car. Bring hiking boots, warm clothes, rain gear, and a backpack, since Telluride is surrounded by beautiful mountains. Unless otherwise arranged with one of the organizers, we expect participants to stay for the entire duration of this three week workshop. FINANCIAL ARRANGEMENTS: Notification of acceptances will be mailed out around the April 15th, 2014. The Workshop covers all your accommodations and facilities costs for the 3 weeks duration. You are responsible for your own travel to the Workshop, however, sponsored fellowships will be available as described below to further subsidize your cost. Registration Fees: For expenses not covered by federal funds, a Workshop registration fee is required. The fee is $1250 per participant for the 3-week Workshop. This is expected from all participants at the time of acceptance. Accommodations: The cost of a shared condominium, typically a bedroom in a shared condo for senior participants or a shared room for students, will be covered for all academic participants. Upgrades to a private rooms or condos will cost extra. Participants from National Laboratories and Industry are expected to pay for these condominiums. Fellowships: This year we will offer two Fellowships to subsidize your costs: Qualcomm Corporation Fellowship: Three non-corporate participants will have their accommodation and registration fees ($2750) directly covered by Qualcomm, and will be reimbursed for travel costs up to $500. Additional generous funding from Qualcomm will provide $5000 to help organize and stage the Workshop. EU-CSNII Fellowship (http://csnetwork.eu/) which is funded by the 7th Research Framework Program FP7-ICT-CSNII-601167: The top 8 EU applicants will be reimbursed for their registration fees ($1250), subsistence/travel subsidy (up to Euro 2000) and accommodations cost ($1500). The registration and accommodation costs will go directly to the INE (the INE will reimburse the participant's registration fees after receipt from CSNII), while the subsistence/travel reimbursement will be provided directly to the participants by the CSNII at the University of Pompeu Fabra, Barcelona, Spain. HOW TO APPLY: Applicants should be at the level of graduate students or above (i.e. postdoctoral fellows, faculty, research and engineering staff and the equivalent positions in industry and national laboratories). We actively encourage women and minority candidates to apply. Anyone interested in proposing or discussing specific projects should contact the appropriate topic leaders directly. The application website is (after February 7th, 2014): ine-web.org/telluride-conference-2014/apply-info Application information needed: Contact email address. First name, Last name, Affiliation, valid e-mail address. Curriculum Vitae (a short version, please). One page summary of background and interests relevant to the workshop, including possible ideas for workshop projects. Please indicate which topic areas you would most likely join. Two letters of recommendation (uploaded directly by references). Applicants will be notified by e-mail. 7th February, 2014 - Applications accepted on website 2nd April, 2014 - Applications Due 15th April, 2014 - Notification of Acceptance -- Ralph Etienne-Cummings, PhD, FIEEE Professor Department of Electrical and Computer Engineering Computational Sensor Motor Systems Lab -------------- next part -------------- An HTML attachment was scrubbed... URL: