From tibs at stat.Stanford.EDU Wed Aug 1 01:14:22 2001 From: tibs at stat.Stanford.EDU (Rob Tibshirani) Date: Tue, 31 Jul 2001 22:14:22 -0700 (PDT) Subject: book announcement Message-ID: <200108010514.WAA744941@rgmiller.Stanford.EDU> Book announcement: The Elements of Statistical Learning- data mining, inference and prediction 536p (in full color) Trevor Hastie, Robert Tibshirani, and Jerome Fridman Springer-Verlag, 2001 For full details see http://www-stat.stanford.edu/ElemStatLearn Here is a brief description: During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of Statistics, and spawned new areas such as data mining, machine learning and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data-mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting --- the first comprehensive treatment of this topic in any book. Jerome Friedman, Trevor Hastie, and Robert Tibshirani are Professors of Statistics at Stanford University. They are prominent researchers in this area: Friedman is the (co-)inventor of many data-mining tools including CART, MARS, and projection pursuit. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modelling software in S-PLUS, and invented principal curves and surfaces. Tibshirani proposed the Lasso and co-wrote the best selling book ``An Introduction to the Bootstrap''. ********************************************** Rob Tibshirani, Dept of Health Research & Policy and Dept of Statistics HRP Redwood Bldg Stanford University Stanford, California 94305-5405 phone: HRP: 650-723-7264 (Voice mail), Statistics 650-723-1185 FAX 650-725-8977 tibs at stat.stanford.edu http://www-stat.stanford.edu/~tibs From ingber at ingber.com Thu Aug 2 17:59:56 2001 From: ingber at ingber.com (Lester Ingber) Date: Thu, 2 Aug 2001 16:59:56 -0500 Subject: Paper: Probability tree algorithm for general diffusion processes Message-ID: <20010802165956.A13979@ingber.com> The following preprint is available: %A L. Ingber %A C. Chen %A R.P. Mondescu %A D. Muzzall %A M. Renedo %T Probability tree algorithm for general diffusion processes %J Physical Review E %P (to be published) %D 2001 %O URL http://www.ingber.com/path01_pathtree.ps.gz ABSTRACT Motivated by path-integral numerical solutions of diffusion processes, PATHINT, we present a new tree algorithm, PATHTREE, which permits extremely fast accurate computation of probability distributions of a large class of general nonlinear diffusion processes. -- Prof. Lester Ingber ingber at ingber.com www.ingber.com ingber at alumni.caltech.edu www.alumni.caltech.edu/~ingber From allan at biomedica.org Thu Aug 2 18:24:25 2001 From: allan at biomedica.org (Allan Kardec Barros) Date: Thu, 02 Aug 2001 19:24:25 -0300 Subject: Extraction of Specific Signals with Temporal Structure Message-ID: <3B69D319.562840E6@biomedica.org> Apologies if you receive multiple copies of this message. Dear Everyone, I would like to announce the following paper, recently published in Neural Computation. For those familiar with ICA, the difference in this algorithm is basically that, given some simple assumptions, we prove that the permutation problem can be avoided, while the algorithm is quite simple and based on second order statistics, which does not require that at most one signal to be Gaussian. Please feel free to mail me requesting either PS or PDF copies of our work. Best Regards, ak. TITLE: Extraction of Specific Signals with Temporal Structure. AUTORS: A. K. Barros and A. Cichocki. ABSTRACT: In this work we develop a very simple batch learning algorithm for semi-blind extraction of a desired source signal with temporal structure from linear mixtures. Although we use the concept of sequential blind extraction of sources and independent component analysis (ICA), we do not carry out the extraction in a completely blind manner neither we assume that sources are statistically independent. In fact, we show that the {\it a priori} information about the auto-correlation function of primary sources can be used to extract the desired signals (sources of interest) from their linear mixtures. Extensive computer simulations and real data application experiments confirm the validity and high performance of the proposed algorithm. From ted.carnevale at yale.edu Fri Aug 3 12:07:49 2001 From: ted.carnevale at yale.edu (Ted Carnevale) Date: Fri, 03 Aug 2001 12:07:49 -0400 Subject: NEURON course at SFN 2001 meeting Message-ID: <3B6ACC55.93EBBAD1@yale.edu> Short Course Announcement USING THE NEURON SIMULATION ENVIRONMENT Satellite Symposium, Society for Neuroscience Meeting 9 AM - 5 PM on Saturday, Nov. 10, 2001 Speakers: N.T. Carnevale, M.L. Hines, J.W. Moore, and G.M. Shepherd This 1 day course with lectures and live demonstrations will present information essential for teaching and research applications of NEURON, an advanced simulation environment that handles realistic models of biophysical mechanisms, individual neurons, and networks of cells. The emphasis is on practical issues that are key to the most productive use of this powerful and convenient modeling tool. Features that will be covered include: constructing and managing models with the CellBuilder, Network Builder, and Linear Circuit Builder importing detailed morphometric data using the Multiple Run Fitter to optimize models with high-dimensional parameter spaces database resources for empirically-based modeling Each registrant will a comprehensive set of notes which include material that has not appeared elsewhere in print. For more information see the course's WWW pages at http://www.neuron.yale.edu/sd2001.html --Ted Supported in part by the National Science Foundation. Opinions expressed are those of the authors and not necessarily those of the Foundation. From Alton.Ford at tmp.com Fri Aug 3 18:14:06 2001 From: Alton.Ford at tmp.com (Ford, Alton) Date: Fri, 3 Aug 2001 17:14:06 -0500 Subject: Postdoc positions at Los Alamos National Laboratory Message-ID: Postdoctoral Positions in Experimental and Computational Neuroscience The Biophysics Group (http://www.biophysics.lanl.gov/) in the Physics Division at Los Alamos National Laboratory seeks several postdoctoral candidates in the areas of experimental and computational neuroscience. Existing projects include recording of fast optical transients from neural tissue and the development of associated high speed data acquisition systems, imaging devices and optical technology, analysis of evoked MEG and fMRI signals, computational modeling of information processing within the biological neural systems, and collaborative work on the development of a retinal prosthetic device. Successful candidates could combine work in several of these areas. For further technical information, contact Dr. John George at jsg at lanl.gov. A Ph.D. in Physics, Electrical Engineering, Biology, or a related discipline completed within the last three years or soon to be completed is required. Current starting salaries range from $54,100 - $58,300. Further details about the Postdoctoral Program may be found at: http://www.hr.lanl.gov/postdoc/. For consideration, submit a resume and publications list with a cover letter outlining current research interests, including contact information for three references, to postdoc-jobs at lanl.gov (reference PD017639), or submit two copies to: Postdoc Program Office, PD017639, MS-P290, Los Alamos National Laboratory, Los Alamos, NM 87545. Los Alamos National Laboratory is operated by the University of California for the U.S. Department of Energy. AA/EOE Alton Ford Account Executive tmp.worldwide Advertising & Communications 3032 Bunker Hill Lane, Suite 207 Santa Clara, CA 95054 * 408.844.0150 * 408.496.6704 fax * alton.ford at tmp.com www.tmp.com Compliment your recruitment advertising with Web Dragon! ...A service provided by TMP Worldwide, in which a team of our live professionals mine through the millions of resumes on the Internet to find qualified resumes to meet your specified recruitment needs. There are more resumes online than ever. Fill your pipeline with quality resumes and let TMP do the work for you! Please contact me for more information. From juergen at idsia.ch Mon Aug 6 12:20:15 2001 From: juergen at idsia.ch (juergen@idsia.ch) Date: Mon, 6 Aug 2001 18:20:15 +0200 Subject: PhD fellowship Message-ID: <200108061620.SAA08768@ruebe.idsia.ch> I am seeking a PhD student for research on state-of-the-art recurrent neural networks. Please see http://www.idsia.ch/~juergen/phd2001.html Interviews also possible at ICANN 2001 (Aug 21-25) in Vienna or at the ICANN recurrent net workshop: http://www.idsia.ch/~doug/icann/index.html ------------------------------------------------- Juergen Schmidhuber director IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland juergen at idsia.ch www.idsia.ch/~juergen From CogSci at psyvax.psy.utexas.edu Tue Aug 7 13:34:51 2001 From: CogSci at psyvax.psy.utexas.edu (Cognitive Science Society) Date: Tue, 07 Aug 2001 12:34:51 -0500 Subject: Richard M. Shiffrin awarded the Rumelhart Prize Message-ID: <5.0.0.25.2.20010807123359.00b05848@psy.utexas.edu> Richard M. Shiffrin Chosen to Receive the David E. Rumelhart Prize for Contributions to the Formal Analysis of Human Cognition The Glushko-Samuelson Foundation and the Cognitive Science Society are pleased to announce that Richard M. Shiffrin has been chosen as the second recipient of the $100,000 David E. Rumelhart Prize, awarded annually for an outstanding contribution to the formal analysis of human cognition. Shiffrin will receive this prize and give the Prize Lecture at the 2002 Meeting of the Cognitive Science Society, at George Mason University, August 7-11, 2002. Shiffrin has made many contributions to the modeling of human cognition in areas ranging from perception to attention to learning, but is best known for his long-standing efforts to develop explicit models of human memory. His most recent models use Bayesian, adaptive approaches, building on previous work but extending it in a critical new manner, and carrying his theory beyond explicit memory to implicit learning and memory processes. The theory has been evolving for about 35 years, and as a result represents a progression similar to the best theories seen in any branch of science. Shiffrin's major effort began in 1968, in a chapter with Atkinson [1] that laid out a model of the components of short- and long-term memory and described the processes that control the operations of memory. The Atkinson-Shiffrin model encapsulated empirical and theoretical results from a very large number of publications that modeled quantitatively the relation of short- to long-term memory. It achieved its greatest success by showing the critical importance---and the possibility---of modeling the control processes of cognition. This chapter remains one of the most cited works in the entire field of psychology. Shiffrin's formal theory was taken forward in a quantum leap in 1980 [2] and 1981 [3] with the SAM (Search of Associative Memory) model. This was a joint effort with Jeroen Raaijmakers, then a graduate student. The SAM model quantified the nature of retrieval from long-term memory, and characterized reCALL as a memory search with cycles of sampling and recovery. The SAM theory precisely incorporates the notions of interactive cue combination that are now seen to lie at the heart of memory retrieval. Another major quantum step occurred in 1984 [4] when the theory was extended to recognition memory. With another former student, Gary Gillund, Shiffrin initiated what has become the standard approach to recognition memory, in which a decision is based on summed activation of related memory traces. It was a major accomplishment that the same retrieval activations that had been used in the recall model could be carried forward and used to predict a wide range of recognition phenomena. The next major step occurred in 1990, when Shiffrin published two articles on the list-length effect with his student Steve Clark and his colleague, Roger Ratcliff [5, 6]. This research was of critical importance in that it established clearly that experience leads to the differentiation, rather than the mere stregthening, of the representations of items in memory. In 1997, the theory evolved in a radical direction in an important paper with another former student, Mark Steyvers [7]. Although the changes were fundamental, the new model retained the best concepts of its predecessors, so that the previous successful predictions were also a part of the new theory. REM added featural representations, to capture similarity relations among items in memory. Building on earlier ideas by John Anderson, and related ideas developed in parallel by McClelland and Chappell, Shiffrin used Bayesian principles of adaptive and optimal decision making under constraints to guide the selection of the quantitative form of the activation functions. In addition, storage principles were set forth that provided mechanisms by which episodic experience could coalesce over development and experience into permanent non-contextualized knowledge. This latter development allowed the modeling of implicit memory phenomena, in work that is just now starting to appear in many journals, including a theory of long-term priming [with Schooler and Raaijmakers, 8] and a theory of short-term priming [with his student David Huber and others, 9]. The short-term priming research showed that the direction of priming can be reversed by extra study given to particular primes, leading to another conceptual breakthrough. A new version of the REM model explains this and other findings by assuming that some prime features are confused with test item features, and that the system attempts to deal with this situation optimally by appropriate discounting of evidence from certain features. Biographical Information Shiffrin received his Ph. D. from the Mathematical Psychology Program in the Department of Psychology at Stanford University in 1968, the year after Rumelhart received his degree from the same program. Since 1968 he has been on the faculty of the Department of Psychology at Indiana University, where he is now the Luther Dana Waterman Professor of Psychology and Director of the Cognitive Science Program. Shiffrin has accumulated many honors, including membership in the National Academy of Sciences, the American Academy of Arts and Sciences, the Howard Crosby Warren Award of the Society of Experimental Psychologists, and a MERIT Award from the National Institute of Mental Health. Shiffrin has served the field as editor of the Journal of Experimental Psychology: Learning Memory and Cognition, and as a member of the governing boards of several scientific societies. Cited Articles By Richard M. Shiffrin [1] Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence and J. T. Spence (Eds.), The Psychology of Learning and Motivation: Advances in Research and Theory (Vol. 2, pp. 89-195). New York: Aaademic Press. [2] Raaijmakers, J. G. W., & Shiffrin, R. M. (1980). SAM: A theory of probabilistic search of associative memory. In Bower, G. H. (Ed.), The Psychology of Learning and Motivation, Vol. 14, 207-262. New York: Academic Press. [3] Raaijmakers, J. G. W., & Shiffrin, R. M. (1981). Search of associative memory. Psychological Review, 88, 93-134. [4] Gillund, G., & Shiffrin, R. M. (1984). A retrieval model for both recognition and recall. Psychological Reviw, 91, 1-67. [5] Ratcliff, R., Clark, S., & Shiffrin, R. M. (1990). The list-strength effect: I. Data and discussion. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 163-178. [6] Shiffrin, R. M., Ratcliff, R., & Clark, S. (1990). The list-strength effect: II. Theoretical mechanisms. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 179-195. [7] Shiffrin, R. M., & Steyvers, M. (1997). A model for recognition memory: REM: Retrieving effectively from memory. Psychonomic Bulletin and Review, 4 (2), 145-166. [8] Schooler, L., Shiffrin, R. M., & Raaijmakers, J. G. W. (2001). A model for implicit effects in perceptual identification. Psychological Review, 108, 257-272. [9] Huber, D. E., Shiffrin, R. M., Lyle, K. B., & Ruys, K. I. (2001). Perception and preference in short-term word priming. Psychological Review, 108, 149-182. ================================================================ Geoffrey E. Hinton Named First Recipient of the David E. Rumelhart Prize May 3, 2001 Today the Glushko-Samuelson foundation and the Cognitive Science Society jointly announced that Geoffrey E. Hinton has been named the first recipient of the David E. Rumelhart Prize for contemporary contributions to the formal analysis of human cognition. Hinton, the Director of the Gatsby Computational Neuroscience Unit at University College, London, was chosen from a large field of outstanding nominees because of his seminal contributions to the understanding of neural networks. "Hinton's insights into the analysis of neural networks played a central role in launching the field in the mid-1980's" said Professor James McClelland of Carnegie Mellon University, Chair of the Prize Selection Committee, "Geoff also played a major role in conveying the relevance of neural networks to higher-level cognition." Professor Lawrence Barsalou of Emory University, President of the Cognitive Science Society, agreed with this assessment. "Hinton's contributions to Cognitive Science have been pivotal", said Barsalou. "As the first recipient he sets a great example for future awards." Hinton will receive the prize, which includes a monetary award of $100,000, at the annual meeting of the Society in Edinburgh, Scotland, in early August, 2001. The Rumelhart prize acknowledges intellectual generosity and effective mentoring as well as scientific insight. "Dave Rumelhart gave away many scientific ideas, and made important contributions to the work of many of his students and co-workers" said Robert J. Glushko, President of the Glushko-Samuelson foundation. He added "Hinton stands out not only for his own contributions but for his exemplary record in mentoring young scientists." A total of eighteen graduate students have received their Ph. D.'s under Hinton's supervision. In conjunction with naming Hinton as the first recipient of the David E. Rumelhart Prize, the Glushko-Samuelson foundation announced that the prize will be awarded on an annual basis, instead of biennially. "This change reflects the number of outstanding scientists who were nominated for the award" noted Glushko. "I am pleased that my foundation can play a role in honoring their contributions to cognitive science." The second recipient of the Prize will be announced at the Edinburgh meeting of the society, and will give the prize lecture at the next annual meeting, which will be at George Mason University in August, 2002. For further information, please visit the David E. Rumelhart Prize web site: http://www.cnbc.cmu.edu/derprize/DerPrize2001.html or contact: Robert J. Glushko, 415-644-8731 James L. McClelland, 412-268-3157 ---------- Cognitive Science Society c/o Tanikqua Young Department of Psychology University of Texas Austin, TX 78712 Phone: (512) 471-2030 Fax: (512) 471-3053 ---------- From amari at brain.riken.go.jp Thu Aug 9 00:57:01 2001 From: amari at brain.riken.go.jp (Shun-ichi Amari) Date: Thu, 9 Aug 2001 13:57:01 +0900 Subject: FW: new book on Information Geometry Message-ID: ******************** ???? ?????????????????????????? ????????????????????????? ???????? ????????????????? Dear Connectionists I have announced the publication of the book "Methods of Information Geometry" but heard complaints that the book is out of stock. Now they printed further, and you can order from AMS or Oxford University Press through bookshops. ************* It is my pleasure to announce the publication of a book on Information Geometry. I have been often asked if there is a good book on information geometry to know its general perspectives. Here it is. S.Amari and H.Nagaoka, Methods of Information Geometry, AMS Translations of Mathematical Monographs, vol 191 (translated by Daishi Harada) American Mathematical Society (AMS) and Oxford University Press, 206 + x pages, 2000. (See http://www.ams.org/) ******************** Shun-ichi Amari Vice Director, RIKEN Brain Science Institute Laboratory for Mathematical Neuroscience Research Group on Brain-Style Information Systems tel: +81-(0)48-467-9669; fax: +81-(0)48-467-9687 amari at brain.riken.go.jp http://www.bsis.brain.riken.go.jp/ From orhan at ait-tech.com Wed Aug 8 14:16:02 2001 From: orhan at ait-tech.com (Orhan Karaali) Date: Wed, 8 Aug 2001 14:16:02 -0400 Subject: Research Scientist position at Advanced Investment Technology Message-ID: ADVANCED INVESTMENT TECHNOLOGY, INC. www.ait-tech.com Advanced Investment Technology, Inc. (AIT) is a registered investment advisor based in Clearwater, Florida focusing on institutional domestic equity asset management. Our partners include Boston-based State Street Global Advisors, a global leader in institutional financial services, and Amsterdam-based Stichting Pensioenfonds ABP, one of the world's largest pension plans. AIT's reputation as an innovative entrepreneur within the asset management community is built upon the research and development of nontraditional quantitative stock valuation techniques (neural networks and genetic algorithms) for which a patent was issued in 1998. POSITION: RESEARCH SCIENTIST The position will involve developing software and valuation algorithms for stock selection and portfolio management. Job responsibilities include database development, running weekly production jobs, working with financial data vendor feeds, contributing to financial research projects, and developing applications in the areas of multifactor stock models. AIT uses Windows 2000; Visual Studio 6 and Visual Studio Net; MS SQL 2000; C++ STL; OLE DB; XML; SOAP; and OLAP technologies. Minimum Qualifications: Bachelors Degree in Computer Science or a related field Masters Degree in Computer Science or MBA Very strong C++ and STL background Working knowledge of SQL Bonus Qualifications: Familiarity with financial data and asset management Experience developing object oriented software with C++ and STL Familiarity with Microsoft Visual Studio Knowledge of machine learning algorithms (NN, GA, GP, SVM) To apply, please send your resume to: E-mail: orhan at ait-tech.com Fax: (727) 799-1232 (Attn: Orhan Karaali) From norman at psych.colorado.edu Sat Aug 11 00:03:12 2001 From: norman at psych.colorado.edu (Ken Norman) Date: Fri, 10 Aug 2001 22:03:12 -0600 (MDT) Subject: new paper: modeling hippocampal and neocortical contributions to recognition memory Message-ID: Dear Connectionists, The following technical report is now available for downloading as: ftp://grey.colorado.edu/pub/oreilly/papers/NormanOReilly01_recmem.pdf webpage: http://psych.colorado.edu/~oreilly/pubs-abstr.html#01_recmem Modeling Hippocampal and Neocortical Contributions to Recognition Memory: A Complementary Learning Systems Approach Kenneth A. Norman and Randall C. O'Reilly Department of Psychology University of Colorado Boulder, CO 80309 ICS Technical Report 01-02 Abstract: We present a computational neural network model of recognition memory based on the biological structures of the hippocampus and medial temporal lobe cortex (MTLC), which perform complementary learning functions. The hippocampal component of the model contributes to recognition by recalling specific studied details. MTLC can not support recall, but it is possible to extract a scalar familiarity signal from MTLC that tracks how well the test item matches studied items. We present simulations that establish key qualitative differences in the operating characteristics of the hippocampal recall and MTLC familiarity signals, and we identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the two signals. We also use the model to address the stochastic relationship between recall and familiarity (i.e., are they independent), and the effects of partial vs. complete hippocampal lesions on recognition. From yokoy at brain.riken.go.jp Tue Aug 14 02:12:01 2001 From: yokoy at brain.riken.go.jp (Yoko Yamaguchi) Date: Tue, 14 Aug 2001 15:12:01 +0900 Subject: Postdoctorial/ technical staff positions in cognitive science and computational neurosicence Message-ID: Please post: POSTDOCTORAL SCIENTIST/ TECHNICAL STAFF POSITIONS at RIKEN BSI Laboratory for Dynamics of Emergent Intelligence, Brain-Style Intelligence Research Group, RIKEN Brain Science Institute (BSI) invites applicants for postdoctoral and technical staff scientists in the fields of cognitive science and computational neurosicence. Our objective is to clarify the neural principle for the dynamics of emergent intelligence in novel situations. Particular emphasis is given to synchronization of oscillations in hierarchical neural networks. For further information see http://www.dei.brain.riken.go.jp/ Applicants for postdoctoral positions must have a PhD. Technical staff are expected to have a bachelors or masters degree. Applicant should submit a full CV detailing education and experience, attached with your photograph, in addition to a complete bibliography of publications. The names, addresses, email addresses of two referees must also be supplied. Send all applications to: Contact:: Dr. Yoko yamaguchi (Laboratory Head) Lab. for Dynamics of Emergent Intelligence Brain Science Institute, RIKEN 2-1 Hirosawa, Wako, Saitama, 351-0198 Japan FAX: +81-48-467-6938 E-mail : yokoy at brain.riken.go.jp ------------------------------------------------------------------- Yoko Yamaguchi Lab. for Dynamics of Emergent Intelligence RIKEN Brain Science Institute(BSI) From bbs at bbsonline.org Mon Aug 13 16:27:04 2001 From: bbs at bbsonline.org (Stevan Harnad - Behavioral & Brain Sciences (Editor)) Date: Mon, 13 Aug 2001 16:27:04 -0400 Subject: Norman: Two Visual Systems -- BBS Call for Commentators Message-ID: Dear Dr. Connectionists List User, Below is the abstract of a forthcoming BBS target article Two Visual Systems and Two Theories of Perception: An Attempt to Reconcile the Constructivist and Ecological Approaches by Joel Norman http://www.bbsonline.org/Preprints/Norman/ http://psy.haifa.ac.il/~maga/tvs&ttp.pdf This article has been accepted for publication in Behavioral and Brain Sciences (BBS), an international, interdisciplinary journal providing Open Peer Commentary on important and controversial current research in the biobehavioral and cognitive sciences. Commentators must be BBS Associates or nominated by a BBS Associate. To be considered as a commentator for this article, to suggest other appropriate commentators, or for information about how to become a BBS Associate, please reply by EMAIL within three (3) weeks to: calls at bbsonline.org The Calls are sent to 8000 BBS Associates, so there is no expectation (indeed, it would be calamitous) that each recipient should comment on every occasion! Hence there is no need to reply except if you wish to comment, or to nominate someone to comment. If you are not a BBS Associate, please approach a current BBS Associate (there are currently over 10,000 worldwide) who is familiar with your work to nominate you. All past BBS authors, referees and commentators are eligible to become BBS Associates. A full electronic list of current BBS Associates is available at this location to help you select a name: http://www.bbsonline.org/Instructions/assoclist.html If no current BBS Associate knows your work, please send us your Curriculum Vitae and BBS will circulate it to appropriate Associates to ask whether they would be prepared to nominate you. (In the meantime, your name, address and email address will be entered into our database as an unaffiliated investigator.) To help us put together a balanced list of commentators, please give some indication of the aspects of the topic on which you would bring your areas of expertise to bear if you were selected as a commentator. To help you decide whether you would be an appropriate commentator for this article, an electronic draft is retrievable from the online BBSPrints Archive, at the URL that follows the abstract below. _____________________________________________________________ Two Visual Systems and Two Theories of Perception: An Attempt to Reconcile the Constructivist and Ecological Approaches Joel Norman Department of Psychology University of Haifa Haifa, Israel jnorman at psy.haifa.ac.il KEYWORDS: Visual perception theories, ecological, constructivist, two visual systems, space perception, size perception, dual-process approach ABSTRACT: The two contrasting theoretical approaches to visual perception, the constructivist and the ecological, are briefly presented and illustrated through their analyses of space perception and size perception. Earlier calls for their reconciliation and unification are reviewed. Neurophysiological, neuropsychological, and psychophysical evidence for the existence of two quite distinct visual systems, the ventral and the dorsal, is presented. These two perceptual systems differ in their functions; the ventral systems central function is that of identification, while the dorsal system is mainly engaged in the visual control of motor behavior. The strong parallels between the ecological approach and the functioning of the dorsal system and between the constructivist approach and the functioning of the ventral system are noted. It is also shown that the experimental paradigms used by the proponents of these two approaches match the functions of the respective visual systems. A dual-process approach to visual perception emerges from this analysis, with the ecological-dorsal process transpiring mainly without conscious awareness, while the constructivist-ventral process is normally conscious. Some implications of this dual-process approach to visual-perceptual phenomena are presented, with emphasis on space perception. http://www.bbsonline.org/Preprints/Norman/ http://psy.haifa.ac.il/~maga/tvs&ttp.pdf ___________________________________________________________ Please do not prepare a commentary yet. Just let us know, after having inspected it, what relevant expertise you feel you would bring to bear on what aspect of the article. We will then let you know whether it was possible to include your name on the final formal list of invitees. _______________________________________________________________________ *** SUPPLEMENTARY ANNOUNCEMENTS *** (1) The authors of scientific articles are not paid money for their refereed research papers; they give them away. What they want is to reach all interested researchers worldwide, so as to maximize the potential research impact of their findings. Subscription/Site-License/Pay-Per-View costs are accordingly access-barriers, and hence impact-barriers for this give-away research literature. There is now a way to free the entire refereed journal literature, for everyone, everywhere, immediately, by mounting interoperable university eprint archives, and self-archiving all refereed research papers in them. Please see: http://www.eprints.org http://www.openarchives.org/ http://www.dlib.org/dlib/december99/12harnad.html --------------------------------------------------------------------- (2) All authors in the biobehavioral and cognitive sciences are strongly encouraged to self-archive all their papers in their own institution's Eprint Archives or in CogPrints, the Eprint Archive for the biobehavioral and cognitive sciences: http://cogprints.soton.ac.uk/ It is extremely simple to self-archive and will make all of our papers available to all of us everywhere, at no cost to anyone, forever. Authors of BBS papers wishing to archive their already published BBS Target Articles should submit it to BBSPrints Archive. Information about the archiving of BBS' entire backcatalogue will be sent to you in the near future. Meantime please see: http://www.bbsonline.org/help/ and http://www.bbsonline.org/Instructions/ --------------------------------------------------------------------- (3) Call for Book Nominations for BBS Multiple Book Review In the past, Behavioral and Brain Sciences (BBS) had only been able to do 1-2 BBS multiple book treatments per year, because of our limited annual page quota. BBS's new expanded page quota will make it possible for us to increase the number of books we treat per year, so this is an excellent time for BBS Associates and biobehavioral/cognitive scientists in general to nominate books you would like to see accorded BBS multiple book review. (Authors may self-nominate, but books can only be selected on the basis of multiple nominations.) It would be very helpful if you indicated in what way a BBS Multiple Book Review of the book(s) you nominate would be useful to the field (and of course a rich list of potential reviewers would be the best evidence of its potential impact!). *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* Please note: Your email address has been added to our user database for Calls for Commentators, the reason you received this email. If you do not wish to receive further Calls, please feel free to change your mailshot status through your User Login link on the BBSPrints homepage. Check the helpfiles for details of how to obtain your username and password. http://www.bbsonline.org/ For information about the mailshot, please see the help file at: http://www.bbsonline.org/help/node5.html#mailshot *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* From murphyk at cs.berkeley.edu Tue Aug 14 20:42:21 2001 From: murphyk at cs.berkeley.edu (Kevin Murphy) Date: Tue, 14 Aug 2001 17:42:21 -0700 Subject: OpenBayes Message-ID: <3B79C56D.43E951A5@cs.berkeley.edu> Richard Dybowski formed the OpenBayes discussion group/email list on 17 January 2001. The goal is to discuss the development of an open source library for probabilistic graphical models. We had our first meeting at the recent UAI conference in Seattle. The only concrete decision reached was that we should advertise the existence of this group more widely - hence this email. For more details on the OpenBayes project, please see http://HTTP.CS.Berkeley.EDU/~murphyk/OpenBayes/index.html This page includes a list of people who attended the meeting, more details on the project's goals, achievements to date, ways you can subscribe to the list and/or contribute code, etc. Kevin Murphy P.S. If you have problems subscribing to the list, please send email to openbayes-owner at egroups.com, not to me! I am not the moderator. From neted at anc.ed.ac.uk Wed Aug 15 05:54:39 2001 From: neted at anc.ed.ac.uk (Network Editor) Date: Wed, 15 Aug 2001 10:54:39 +0100 Subject: NETWORK: Computation in Neural Systems Message-ID: <15226.18143.81476.664143@gargle.gargle.HOWL> Here is the contents page for the current issue of NETWORK: Computation in Neural Systems. NETWORK publishes original research work on theoretical and computational aspects of the development and functioning of the nervous system, at all levels of analysis, particularly at the network, cellular and subcellular levels. Professor David Willshaw Editor-in-Chief NETWORK: Computation in Neural Systems Institute for Adaptive & Neural Computation Division of Informatics University of Edinburgh 5 Forrest Hill Edinburgh EH1 2QL UK Tel: +44-(0)131-650 4404 Fax: +44-(0)131-650 4406 Email: neted at anc.ed.ac.uk ======================================================================== NETWORK: COMPUTATION IN NEURAL SYSTEMS - VOLUME 12, ISSUE 3, AUGUST 2001 Special issue featuring selected papers from the Natural Stimulus Statistics Workshop, October 2000, Cold Spring Harbor, USA EDITORIALS Publishing papers in Network: Special Issues D J Willshaw (p 235) Natural stimulus statistics P Reinagel and S Laughlin (pp 237-240) PAPERS Redundancy reduction revisited H Barlow (pp 241-253) Characterizing the sparseness of neural codes B Willmore and D J Tolhurst (pp 255-270) Beats, kurtosis and visual coding M G A Thomson (pp 271-287) Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli F E Theunissen, S V David, N C Singh, A Hsu, W E Vinje and J L Gallant (pp 289-316) Neural coding of naturalistic motion stimuli G D Lewen, W Bialek and R R de Ruyter van Steveninck (pp 317-329) Nonlinear and extra-classical receptive field properties and the statistics of natural scenes C Zetzsche and F R?hrbein (pp 331-350) Neuronal processing of behaviourally generated optic flow: experiments and model simulations R Kern, M Lutterklas, C Petereit, J P Lindemann and M Egelhaaf (pp 351-369) Can recent innovations in harmonic analysis `explain' key findings in natural image statistics? D L Donoho and A G Flesia (pp 371-393) Optimal nonlinear codes for the perception of natural colours T von der Twer and D I A MacLeod (pp 395-407) From colette.faucher at wanadoo.fr Thu Aug 16 05:40:32 2001 From: colette.faucher at wanadoo.fr (colette faucher) Date: Thu, 16 Aug 2001 02:40:32 -0700 Subject: cfp for FLAIRS special track : Categorization and Concept Representation : Models and Implications Message-ID: <3b7b16df3cc49870@amyris.wanadoo.fr> (added by amyris.wanadoo.fr) =========================================================================== FLAIRS 2002 15th International Florida Artificial Intelligence Research Society Conference Pensacola, Florida May 16-18, 2002 Special Track : "Categorization and Concept Representation : Models and Implications" =========================================================================== This track seeks to bring together researchers working on issues related to categorization and concept representation in the areas of Artificial Intelligence and Cognitive Psychology. Topic Description ------------------ Categorization is the process by which distinct entities are treated as equivalent. It is one of the most fundamental and pervasive cognitive activities. It is fundamental because categorization allows us to understand and make predictions about objects and events in our world. The problem of understanding what criteria are used to group together entities in a same category is indeed central in categorization. Though most works in that topic have proposed that perceptual or structural similarity is the "glue" that binds objects of a same category, some psychologists have claimed that similarity is insufficient to account for the acquisition and use of categories and have proposed more abstract forms of criteria that make categories coherent and give them a kind of homogeneity in terms of the entities that belong to them. The different new propositions psychologists have suggested are that objects are grouped together because they facilitate a common goal or serve the same function. Some categories are viewed as coherent because they rest on a theory which explains the commonalities of their elements. Similarity and goals, on one hand, and theories, on the other hand, have not been paid the same attention in computational models of categorization. Similarity-based models abound and the notion of categorization goals has also been exploited in computational models. On the other hand, the notion of an underlying theory that makes a category coherent just begins to be further analyzed and specified. New computational models of categorization reflecting this new tendency are thus expected. The representation of concepts that a categorization system generates is of course intimately tied to the criteria this system uses to group entities into categories, so along with new models of categorization, we expect to see the emergence of new models of concept representation apart from the classical ones deriving from the Aristotelician, the Prototypical and the Exemplar Views. The representation of the entities to categorize plays also an important part in the categorization process. In particular, the context in which the entities occur may influence the way they are classified. The purpose of this track is to bring fresh insights concerning a perhaps revisited notion of similarity, the way goals of categorization influence this process, how the notion of the theory of a concept can be formalized and implemented in computational models of categorization and the implications those elements may have on the representation of concepts. The contributions to this track may be situated in the symbolic approach of categorization or the connectionist one. Contributions in the following sub-topics would be welcomed : - Computational models of similarity, - Computational models of theory-based categorization, - Computational models of similarity-based categorization, - Computational models of human categorization, - Models of concept representation which are relevant as regards to the process of categorization, - Models of concept representation and elicitation, - Formalization of the notion of theory which underlies a category, - Formalization of the context of occurrence of the entities to categorize and its influence on the categorization process. This list is not exclusive provided that the contributions are relevant to the definition of the track specified above. Paper Review and Publication ------------------------------ Only full papers will be considered for the track. Submitted papers will be reviewed by two program committee members. An author for an accepted paper is expected to present the paper in the track. Papers accepted for the track will be published in the FLAIRS 2002 Conference Proceedings. The best papers will be invited for modification, extension and submission to a special issue in an international AI journal. Important dates ---------------- Paper Submission Deadline : November 15, 2001 Notification of Acceptance-Rejection : January 10, 2002 Camera Ready Copy Due : March 4, 2002 Journal Invitation : February 10, 2002 Journal Paper Due : May 10, 2002 Conference Dates : May 16-18, 2002 Program Committee ------------------ David W. Aha, Navy Center for Applied Research in AI, Washington, USA Ralph Bergmann, University of Kaiserslautern, Germany Max Bramer, University of Portsmouth, UK Colette Faucher (Chair), University of Aix-Marseille III, France Paolo Frasconi, University of Florence, Italy Robert L. Goldstone, Indiana University, USA James Hampton, City University, London, UK David Leake, Indiana University, USA Bradley C. Love, University of Texas, USA Paul Mc Kevitt, University of Ulster, Northern Ireland Ryszard S. Michalski, George Mason University, USA Philip Resnik, University of Maryland, USA Lance J. Rips, Northwestern University, USA Steven A. Sloman, Brown University, USA Paper Submission Information ----------------------------- Authors must submit an electronic copy of their complete manuscript of no more than 5 pages. All submissions must be original work. The review will be blind. Author names and affiliations are to appear ONLY on a separate cover page. The presenter (if different) from the first author must be specified on that cover page. All appropriate contact information must be mentioned for each author (e-mail, phone, fax, etc.). Papers must be written using MS Word, RTF or PDF formats according to AAAI's standard format for authors. All submissions must be sent in electronic form to : colette.faucher at iuspim.u-3mrs.fr and colette.faucher at wanadoo.fr For any problem or question, please contact the chair track, Colette Faucher, at : colette.faucher at iuspim.u-3mrs.fr or colette.faucher at wanadoo.fr. Track Website -------------- http://perso.wanadoo.fr/colette.faucher/categorization.html FLAIRS 2002 Website -------------------- http://altair.coginst.uwf.edu/~jkolen/Flairs2002/intro.php3 From wolfskil at MIT.EDU Fri Aug 17 13:17:26 2001 From: wolfskil at MIT.EDU (Jud Wolfskill) Date: Fri, 17 Aug 2001 13:17:26 -0400 Subject: book announcement--Leen Message-ID: <5.0.2.1.2.20010817115831.00ae3e28@hesiod> Hello, I thought readers of the Connectionists List might be interested in this book. For more information please visit http://mitpress.mit.edu/catalog/item/default.asp?sid=59E8DBE7-4980-48C7-A87F-F0917571FB1E&ttype=2&tid=8662 Best, Jud Advances in Neural Information Processing Systems 13 edited by Todd K. Leen, Thomas G. Dietterich, and Volker Tresp The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2000 conference. Todd K. Leen is Professor of Computer Science and Engineering, and of Electrical and Computer Engineering, at Oregon Graduate Institute of Science and Technology. Thomas G. Dietterich is Professor of Computer Science at Oregon State University. Volker Tresp heads a research group at Siemens Corporate Technology in Munich. 7 x 10, 1100 pp., cloth ISBN 0-262-12241-3 Neural Information Processing series A Bradford Book Jud Wolfskill Associate Publicist MIT Press 5 Cambridge Center, 4th Floor Cambridge, MA 02142 617.253.2079 617.253.1709 fax wolfskil at mit.edu From cindy at cns.bu.edu Fri Aug 17 10:04:17 2001 From: cindy at cns.bu.edu (Cynthia Bradford) Date: Fri, 17 Aug 2001 10:04:17 -0400 Subject: Neural Networks 14(6/7): 2001 Special Issue Message-ID: <200108171404.KAA06299@retina.bu.edu> NEURAL NETWORKS 14(6/7) Contents - Volume 14, Numbers 6/7 - 2001 2001 Special Issue "Spiking Neurons in Neuroscience and Technology" Stephen Grossberg, Wolfgang Maass, and Henry Markram, co-editors ------------------------------------------------------------------ Neural assemblies: Technical issues, analysis, and modeling George L. Gerstein and Lyle L. Kirkland Coding properties of spiking neurons: Reverse and cross-correlations Wulfram Gerstner ON-OFF retinal ganglion cells temporally encode OFF/ON sequence Hiroyuki Uchiyama, Koichi Goto, and Hiroyuki Matsunobu Building blocks for electronic spiking neural networks Andre van Schaik Orientation-selective aVLSI spiking neurons Shih-Chii Liu, Jorg Kramer, Giacomo Indiveri, Tobias Delbruck, Thomas Burg, and Rodney Douglas Space-rate coding in an adaptive silicon neuron Kai Hynna and Kwabena Boahen Propagation of cortical synfire activity: Survival probability in single trials and stability in the mean Marc-Oliver Gewaltig, Markus Diesmann, and Ad Aertsen Fokker-Planck approach to the pulse packet propagation in synfire chain H. Cateau and T. Fukai Connection topology dependence of synchronization of neural assemblies on class 1 and 2 excitability Luis F. Lago-Fernandez, Fernando J. Corbacho, and Ramon Huerta Deterministic dynamics emerging from a cortical functional architecture Ralph M. Siegel and Heather L. Read Spike-based strategies for rapid processing Simon Thorpe, Arnaud Delorme, and Rufin van Rullen Zero-lag synchronous dynamics in triplets of interconnected cortical areas D. Chawla, K.J. Friston, and E.D. Lumer Neural timing nets P.A. Cariani Spike-based VLSI modeling of the ILD system in the echolocating bat Timothy Horiuchi and Kai Hynna Pattern separation and synchronization in spiking associative memories and visual areas Andreas Knoblauch and Gunther Palm Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons David H. Goldberg, Gert Cauwenberghs, and Andreas G. Andreou Face identification using one spike per neuron: Resistance to image degradations A. Delorme and S.J. Thorpe Temporal receptive fields, spikes, and Hebbian delay selection Christian Leibold and J. Leo van Hemmen Distributed synchrony in a cell assembly of spiking neurons Nir Levy, David Horn, Isaac Meilijson, and Eytan Ruppin Associative memory in networks of spiking neurons Friedrich T. Sommer and Thomas Wennekers Trajectory estimation from place cell data Nanayaa Twum-Danso and Roger Brockett A pulsed neural network model of bursting in the basal ganglia Mark D. Humphries and Kevin N. Gurney Regularization mechanisms of spiking-bursting neurons P. Varona, J.J. Torres, R. Huerta, H.D.I. Abarbanel, and M.I. Rabinovich Optimal firing rate estimation Michael G. Paulin and Larry F. Hoffman Resonate-and-fire neurons Eugene M. Izhikevich Coherence resonance and discharge time reliability in neurons and neuronal models K. Pakdaman, Seiji Tanabe, and Tetsuya Shimokawa Adaptation in single spiking neurons based on a noise shaping neural coding hypothesis Jonghan Shin The double queue method: A numerical method for integrate-and-fire neuron networks Geehyuk Lee and Nabil H. Farhat A spiking neural network architecture for nonlinear function approximation Nicolangelo Iannella and Andrew D. Back From kenm at uwo.ca Sun Aug 19 16:02:44 2001 From: kenm at uwo.ca (Ken McRae) Date: Sun, 19 Aug 2001 16:02:44 -0400 Subject: Postdoctoral Postion Message-ID: Postdoctoral Fellowship in Psycholinguistics & Computational Modeling I have funding for a two-year Postdoctoral Fellowship in my Cognitive Science laboratory at the University of Western Ontario in London, Ontario, Canada. The stipend is $35,000 per year plus $2,500 per year for conference travel. There are no citizenship restrictions. Our research focuses on the interrelated issues of noun meaning, verb meaning, and sentence processing. Our research integrates theories and methodologies from a number of areas, including: word recognition, semantic memory, concepts and categorization, sentence processing, connectionist modeling, and cognitive neuropsychology. Central to our research program is connectionist modeling of the computation of noun and verb meaning, as well as competition-integration modeling of on-line sentence reading time. Thus, a postdoctoral fellow in my lab will have the opportunity to participate in projects in a number of areas of Cognitive Science. Our department has a number of Cognition faculty, all of whom conduct research related to language processing. Thus, our faculty and graduate students provide a rich research environment. I am also involved in a number of collaborations with researchers from other universities. My lab is well-equipped for both human experimentation and computational modeling. UWO also has a 4T magnet that is used for research only. London is a pleasant city of approximately 350,000, and is located 2 hours drive from either Toronto or Detroit. Note that a reasonable one-bedroom apartment in London costs approximately $500 per month. For further information about our lab, and Cognition at UWO, see: http://www.sscl.uwo.ca/psychology/cognitive/faculty.html If you are interested in this position, please send a cv, a statement of research interests, and 3 letters of reference to me at the address below. Sending all information electronically is preferable. The start-date for this position is flexible. If you would like more information about this position, please contact me directly. *********************************************************** Ken McRae Associate Professor Department of Psychology & Neuroscience Program Social Science Centre University of Western Ontario London, Ontario CANADA N6A 5C2 email: mcrae at uwo.ca http://www.sscl.uwo.ca/psychology/cognitive/mcrae/mcrae.html phone: (519) 661-2111 ext. 84688 fax: (519) 661-3961 *********************************************************** From cohn+jmlr at cs.cmu.edu Mon Aug 20 14:01:50 2001 From: cohn+jmlr at cs.cmu.edu (JMLR) Date: Mon, 20 Aug 2001 14:01:50 -0400 Subject: New paper in the Journal of Machine Learning Research: Bayes Point Machines Message-ID: The Journal of Machine Learning Research (www.jmlr.org) is pleased to announce the availability of a new paper in electronic form. ---------------------------------------- Bayes Point Machines Ralf Herbrich, Thore Graepel and Colin Campbell. Journal of Machine Learning Research 1 (August 2001), pp. 245-279. Abstract Kernel-classifiers comprise a powerful class of non-linear decision functions for binary classification. The support vector machine is an example of a learning algorithm for kernel classifiers that singles out the consistent classifier with the largest margin, i.e. minimal real-valued output on the training sample, within the set of consistent hypotheses, the so-called version space. We suggest the Bayes point machine as a well-founded improvement which approximates the Bayes-optimal decision by the centre of mass of version space. We present two algorithms to stochastically approximate the centre of mass of version space: a billiard sampling algorithm and a sampling algorithm based on the well known perceptron algorithm. It is shown how both algorithms can be extended to allow for soft-boundaries in order to admit training errors. Experimentally, we find that - for the zero training error case - Bayes point machines consistently outperform support vector machines on both surrogate data and real-world benchmark data sets. In the soft-boundary/soft-margin case, the improvement over support vector machines is shown to be reduced. Finally, we demonstrate that the real-valued output of single Bayes points on novel test points is a valid confidence measure and leads to a steady decrease in generalisation error when used as a rejection criterion. This paper and earlier papers in Volume 1 are available electronically at http://www.jmlr.org in PostScript, PDF and HTML formats; a bound, hardcopy edition of Volume 1 will be available later this year. -David Cohn, Managing Editor, Journal of Machine Learning Research ------- This message has been sent to the mailing list "jmlr-announce at ai.mit.edu", which is maintained automatically by majordomo. To subscribe to the list, send mail to listserv at ai.mit.edu with the line "subscribe jmlr-announce" in the body; to unsubscribe send email to listserv at ai.mit.edu with the line "unsubscribe jmlr-announce" in the body. From jf218 at hermes.cam.ac.uk Mon Aug 20 17:15:19 2001 From: jf218 at hermes.cam.ac.uk (Dr J. Feng) Date: Mon, 20 Aug 2001 22:15:19 +0100 (BST) Subject: five years post at cambridge In-Reply-To: <200108171404.KAA06299@retina.bu.edu> Message-ID: The Babraham Institute, Cambridge Computational Neuroscientist/Electrophysiologist (Ref. KK/CNE) Applications are invited for a postdoctoral scientist to join a group of systems neuroscientists within the Laboratory of Cognitive and Developmental Neuroscience investigating how the brain encodes visual and olfactory cues associated with recognition or both social and non-social objects using novel multi-array electrophysiological recording techniques in both rodent and sheep models. This post is available initially for 5 years. It would either suit an individual with primary expertise in computational analysis and modelling of sensory system functioning or an in vivo electrophysiologist with good expertise in computational analysis of complex single-unit data. In both cases there would be significant involvement in carrying out multi-array electrophysiological recording experiments and subsequent data analysis and representation. The individual would also be expected to work closely with electrophysiologists both within the group and the USA and to co-ordinate with other UK-based Computational Neuroscientists involved with the projects. The group already has excellent computational facilities to deal with the large amounts data associated with multi-array recording experiments Informal enquiries on these Neuroscience vacancies should be directed to Dr. Keith Kendrick, Head of Neurobiology Programme: tel: 44(0) 1223 496385, fax. 44(0)1223 496028, e-mail keith.kendrick at bbsrc.ac.uk Starting salary in the range ?19,500 - ?23,000 per annum. Benefits include a non-contributory pension scheme, 25 days leave and 10? public holidays a year. On site Refectory, Nursery and Sports & Social Club as well as free car parking. Further details and an application form available from the Personnel Office, The Babraham Institute, Babraham, Cambridge CB2 4AT. Tel. 01223 496000, e-mail babraham.personnel at bbsrc.ac.uk. The closing date for these positions is 28th September 2001. AN EQUAL OPPORTUNITIES EMPLOYER An Institute supported by the Biotechnology and Biological Sciences Research Council Jianfeng Feng The Babraham Institute Cambridge CB2 4AT UK http://www.cosg.susx.ac.uk/users/jianfeng http://www.cus.cam.ac.uk/~jf218 From wolfskil at MIT.EDU Mon Aug 20 10:25:54 2001 From: wolfskil at MIT.EDU (Jud Wolfskill) Date: Mon, 20 Aug 2001 10:25:54 -0400 Subject: book announcement--O'Reilly Message-ID: <5.0.2.1.2.20010820102443.00a82000@hesiod> I thought readers of the Connectionists List might be interested in this book. For more information please visit http://mitpress.mit.edu/catalog/item/default.asp?sid=16CDFF8A-3F4A-4FB5-B713-D8725D0A6969&ttype=2&tid=3345 Best, Jud Computational Explorations in Cognitive Neuroscience Understanding the Mind by Simulating the Brain Randall C. O'Reilly and Yuko Munakata foreword by James L. McClelland The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprising networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons, and the neural networks incorporate anatomical and physiological properties of the neocortex. Thus the text provides the student with knowledge of the basic biology of the brain as well as the computational skills needed to simulate large-scale cognitive phenomena. The text consists of two parts. The first part covers basic neural computation mechanisms: individual neurons, neural networks, and learning mechanisms. The second part covers large-scale brain area organization and cognitive phenomena: perception and attention, memory, language, and higher-level cognition. The second part is relatively self-contained and can be used separately for mechanistically oriented cognitive neuroscience courses. Integrated throughout the text are more than forty different simulation models, many of them full-scale research-grade models, with friendly interfaces and accompanying exercises. The simulation software (PDP++, available for all major platforms) and simulations can be downloaded free of charge from the Web. Exercise solutions are available, and the text includes full information on the software. Randall C. O'Reilly is Assistant Professor in the Department of Psychology and at the Institute for Cognitive Science at the University of Colorado, Boulder. Yuko Munakata is Assistant Professor in Developmental Cognitive Neuroscience at the University of Denver. 8 x 9, 512 pp., 213 illus., paper ISBN 0-262-65054-1 A Bradford Book Jud Wolfskill Associate Publicist MIT Press 5 Cambridge Center, 4th Floor Cambridge, MA 02142 617.253.2079 617.253.1709 fax wolfskil at mit.edu From ps629 at columbia.edu Tue Aug 21 15:54:59 2001 From: ps629 at columbia.edu (Paul Sajda) Date: Tue, 21 Aug 2001 15:54:59 -0400 Subject: Postdoctoral Position in Computational Neural Modeling Message-ID: <3B82BC93.34337935@columbia.edu> Postdoctoral Position in Computational Neural Modeling--a two year position is available immediately for conducting research in modeling of neural mechanisms for visual scene analysis, with particular applications to spatio-temporal and hyperspectral imagery. A mathematical and computational background is desired, particularly in probabilistic modeling and optimization. This position will be part of a multi-university research team (UPenn, Columbia and MIT) investigating biomimetic methods for analysis of literal and non-literal imagery through a combination of experimental physiology, neuromorphic design and simulation, computational modeling and visual psycophysics. Applicants should send a CV, three representative papers and the names of three references to Prof. Paul Sajda, Department of Biomedical Engineering, Columbia University, 530 W 120th Street, NY, NY 10027. Or email to ps629 at columbia.edu. -- Paul Sajda, Ph.D. Associate Professor Department of Biomedical Engineering 530 W 120th Street Columbia University New York, NY 10027 tel: (212) 854-5279 fax: (212) 854-8725 email: ps629 at columbia.edu http://www.columbia.edu/~ps629 From wolfskil at MIT.EDU Wed Aug 22 14:17:30 2001 From: wolfskil at MIT.EDU (Jud Wolfskill) Date: Wed, 22 Aug 2001 14:17:30 -0400 Subject: book announcement--Opper Message-ID: <5.0.2.1.2.20010822140915.00b083c0@hesiod> I thought readers of the Connectionists List might be interested in this book. For more information please visit http://mitpress.mit.edu/catalog/item/default.asp?sid=5CEC3656-296C-4C48-B6E3-6BDFAC7EBADD&ttype=2&tid=3847 Best, Jud Advanced Mean Field Methods Theory and Practice edited by Manfred Opper and David Saad A major problem in modern probabilistic modeling is the huge computational complexity involved in typical calculations with multivariate probability distributions when the number of random variables is large. Because exact computations are infeasible in such cases and Monte Carlo sampling techniques may reach their limits, there is a need for methods that allow for efficient approximate computations. One of the simplest approximations is based on the mean field method, which has a long history in statistical physics. The method is widely used, particularly in the growing field of graphical models. Researchers from disciplines such as statistical physics, computer science, and mathematical statistics are studying ways to improve this and related methods and are exploring novel application areas. Leading approaches include the variational approach, which goes beyond factorizable distributions to achieve systematic improvements; the TAP (Thouless-Anderson-Palmer) approach, which incorporates correlations by including effective reaction terms in the mean field theory; and the more general methods of graphical models. Bringing together ideas and techniques from these diverse disciplines, this book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling. Manfred Opper is a Reader and David Saad is Professor, the Neural Computing Research Group, School of Engineering and Applied Science, Aston University, UK. 7 x 10, 300 pp. cloth ISBN 0-262-15054-9 Neural Information Processing series Jud Wolfskill Associate Publicist MIT Press 5 Cambridge Center, 4th Floor Cambridge, MA 02142 617.253.2079 617.253.1709 fax wolfskil at mit.edu From abrowne at lgu.ac.uk Thu Aug 23 07:50:50 2001 From: abrowne at lgu.ac.uk (Tony Browne) Date: Thu, 23 Aug 2001 12:50:50 +0100 (GMT Daylight Time) Subject: Connectionist Inference Preprint Message-ID: Apologies if you receive this posting more than once. A preprint is available for download, of the paper 'Connectionist Inference Models' by Antony Browne and Ron Sun (to appear in `Neural Networks'). 62 Pages, 155 References. Abstract: The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling. Keywords: Symbolic inference, resolution, variable binding, localist representations, distributed representations. Download Instructions: Go to http://www.lgu.ac.uk/~abrowne/abrowne.htm and scroll down to the section 'Downloadable Technical Reports and Preprints'. Click on the file to download (in zipped Postscript [190K] or Zipped PDF [228K] format). Comments Welcome If you have problems downloading, please e-mail me. Tony Browne ======================================================= Dr. Antony Browne abrowne at lgu.ac.uk http://www.lgu.ac.uk/~abrowne/abrowne.htm Reader in Intelligent Systems School of Computing, Information Systems & Mathematics London Guildhall University 100 Minories London EC3 1JY, UK Tel: (+44) 0207 320 1307 Fax: (+44) 0207 320 1717 ======================================================= From stefan.wermter at sunderland.ac.uk Thu Aug 23 13:02:13 2001 From: stefan.wermter at sunderland.ac.uk (Stefan.Wermter) Date: Thu, 23 Aug 2001 18:02:13 +0100 Subject: EmerNet book: Emergent Neural Computational Architectures Message-ID: <3B853714.6E58E4CA@sunderland.ac.uk> Emergent Neural Computational Architectures based on Neuroscience Stefan Wermter, Jim Austin, David Willshaw 2001, Springer, Heidelberg, 577p For more detailed information, table of contents, abstracts and chapters see: http://www.his.sunderland.ac.uk/emernet/newbook.html Summary: This book is the result of a series of International Workshops organised by the EmerNet project on Emergent Neural Computational Architectures based on Neuroscience sponsored by the Engineering and Physical Sciences Research Council (EPSRC). The overall aim of the book is to present a broad spectrum of current research into biologically inspired computational systems and hence encourage the emergence of new computational approaches based on neuroscience. It is generally understood that the present approaches for computing do not have the performance, flexibility and reliability of biological information processing systems. Although there is a massive body of knowledge regarding how processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far. The process of developing biologically inspired computerised systems involves the examination of the functionality and architecture of the brain with an emphasis on the information processing activities. Biologically inspired computerised systems address neural computation from the position of both neuroscience, and computing by using experimental evidence to create general neuroscience-inspired systems. The book focuses on the main research areas of modular organisation and robustness, timing and synchronisation, and learning and memory storage. The issues considered as part of these include: How can the modularity in the brain be used to produce large scale computational architectures? How does the human memory manage to continue to operate despite failure of its components? How does the brain synchronise its processing? How does the brain compute with relatively slow computing elements but still achieve rapid and real-time performance? How can we build computational models of these processes and architectures? How can we design incremental learning algorithms and dynamic memory architectures? How can the natural information processing systems be exploited for artificial computational methods? Emergent Neural Computational Architectures based on Neuroscience can be ordered from Springer-Verlag using the booking form and accessed on-line using the appropriate login and password from Springer. http://www.his.sunderland.ac.uk/emernet/newbook.html http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-42363-X -------------------------------------- *************************************** Professor Stefan Wermter Chair for Intelligent Systems University of Sunderland Centre of Informatics, SCET St Peters Way Sunderland SR6 0DD United Kingdom phone: +44 191 515 3279 fax: +44 191 515 3553 email: stefan.wermter at sunderland.ac.uk http://www.his.sunderland.ac.uk/~cs0stw/ http://www.his.sunderland.ac.uk/ **************************************** From rid at ecs.soton.ac.uk Fri Aug 24 05:56:32 2001 From: rid at ecs.soton.ac.uk (Bob Damper) Date: Fri, 24 Aug 2001 10:56:32 +0100 (BST) Subject: Source of a famous quotation ... Message-ID: Dear connectionists, does anyone know the exact source of the famous quotation: ``neural networks are the second best way of solving every problem'' ? I'd be eternally grateful for an answer. Bob. *************************************************************** * R I Damper PhD * * Reader and Head: * * Image, Speech and Intelligent Systems (ISIS) * * Research Group * * Building 1 * * Department of Electronics and Computer Science * * University of Southampton * * Southampton SO17 1BJ * * England * * * * Tel: +44 (0) 23 8059 4577 (direct) * * FAX: +44 (0) 23 8059 4498 * * Email: rid at ecs.soton.ac.uk * * WWW: http://www.ecs.soton.ac.uk/~rid * * * *************************************************************** From gabr-ci0 at wpmail.paisley.ac.uk Fri Aug 24 12:40:10 2001 From: gabr-ci0 at wpmail.paisley.ac.uk (Bogdan Gabrys) Date: Fri, 24 Aug 2001 17:40:10 +0100 Subject: PhD studentship available Message-ID: PhD Studentship Applied Computational Intelligence Research Unit (ACIRU) School of Information and Communication Technologies, University of Paisley, Scotland, UK Applications are invited for a 3 year PhD research studentship which can start from October 2001 and is jointly funded by the University of Paisley (http://www.cis.paisley.ac.uk) and the Lufthansa Systems Berlin GmbH (http://www.lsb.de). The proposed research project will investigate and develop various approaches for combining predictions (forecasts). There is a large potential market for applications offering accurate and reliable predictions ranging from stock market exchange to estimating the demand for sales of goods and services. One such example, which will be looked at in more detail in this project, is an accurate estimation of the demand for various types of airplane tickets. Combination, aggregation and fusion of information are major problems for all kinds of knowledge-based systems, from image processing to decision making, from pattern recognition to automatic learning. Various machine learning and hybrid intelligent techniques will be used for processing and modelling of imperfect data and information utilizing the methodologies like probability, fuzzy, evidence and possibility theories. The student will be joining an enthusiastic and vibrant research group and will be primarily based in the ACIRU in Paisley (near Glasgow), Scotland but two extended visits to the Lufthansa Systems Berlin site in Berlin, Germany are planned in the second and third year of the project. The studentship carries a remuneration of ?7500 tax-free (increased to ?8k and ?9k in the second and third year respectively) and payment of tuition fees paid at Home/EU rate. The stipend may be augmented by a limited amount of teaching. Applicants should have a strong mathematical background and hold a first or upper second class honours degree or equivalent in mathematics, physics, engineering, statistics, computer science or a similar discipline. Additionally the candidate should have strong programming experience using any or combination of C,C++,Matlab or Java. Knowledge of ORACLE will be an advantage. For further details please contact Dr. Bogdan Gabrys, e-mail: gabr-ci0 at paisley.ac.uk. Interested candidates should send a detailed CV and a letter of application with the names and addresses of two referees to: Dr. Bogdan Gabrys, School of Information and Communication Technologies, Div. of Computing and Information Systems, University of Paisley, Paisley PA1 2BE, Scotland, UK. The applications can be also sent by e-mail. ****************************************************** Dr Bogdan Gabrys Applied Computational Intelligence Research Unit Division of Computing and Information Systems University of Paisley High Street, Paisley PA1 2BE Scotland, United Kingdom Tel: +44 (0) 141 848 3752 Fax: +44 (0) 141 848 3542 E-mail: gabr-ci0 at paisley.ac.uk ****************************************************** Legal disclaimer -------------------------- The information transmitted is the property of the University of Paisley and is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. Statements and opinions expressed in this e-mail may not represent those of the company. Any review, retransmission, dissemination and other use of, or taking of any action in reliance upon, this information by persons or entities other than the intended recipient is prohibited. If you received this in error, please contact the sender immediately and delete the material from any computer. -------------------------- From cindy at cns.bu.edu Fri Aug 24 16:34:07 2001 From: cindy at cns.bu.edu (Cynthia Bradford) Date: Fri, 24 Aug 2001 16:34:07 -0400 Subject: Call for Papers: 6th ICCNS Message-ID: <200108242034.QAA17997@retina.bu.edu> Apologies if you receive this more than once. ***** CALL FOR PAPERS ***** SIXTH INTERNATIONAL CONFERENCE ON COGNITIVE AND NEURAL SYSTEMS Tutorials: May 29, 2002 Meeting: May 30 - June 1, 2002 Boston University 677 Beacon Street Boston, Massachusetts 02215 http://www.cns.bu.edu/meetings/ Sponsored by Boston University's Center for Adaptive Systems and Department of Cognitive and Neural Systems with financial support from the National Science Foundation and the Office of Naval Research This interdisciplinary conference has drawn about 300 people from around the world each time that it has been offered. Last year's conference was attended by scientists from 31 countries. The conference is structured to facilitate intense communication between its participants, both in the formal sessions and during its other activities. As during previous years, the conference will focus on solutions to the fundamental questions: How Does the Brain Control Behavior? How Can Technology Emulate Biological Intelligence? The conference will include invited tutorials and lectures, and contributed lectures and posters by experts on the biology and technology of how the brain and other intelligent systems adapt to a changing world. The conference is aimed at researchers and students of computational neuroscience, connectionist cognitive science, artificial neural networks, neuromorphic engineering, and artificial intelligence. A single oral or poster session enables all presented work to be highly visible. Abstract submissions encourage submissions of the latest results. Costs are kept at a minimum without compromising the quality of meeting handouts and social events. CALL FOR ABSTRACTS Session Topics: * vision * spatial mapping and navigation * object recognition * neural circuit models * image understanding * neural system models * audition * mathematics of neural systems * speech and language * robotics * unsupervised learning * hybrid systems (fuzzy, evolutionary, digital) * supervised learning * neuromorphic VLSI * reinforcement and emotion * industrial applications * sensory-motor control * cognition, planning, and attention * other Contributed abstracts must be received, in English, by January 31, 2002. Notification of acceptance will be provided by email by February 28, 2002. A meeting registration fee must accompany each Abstract. See Registration Information below for details. The fee will be returned if the Abstract is not accepted for presentation and publication in the meeting proceedings. Registration fees of accepted Abstracts will be returned on request only until April 19, 2002. Each Abstract should fit on one 8.5" x 11" white page with 1" margins on all sides, single-column format, single-spaced, Times Roman or similar font of 10 points or larger, printed on one side of the page only. Fax submissions will not be accepted. Abstract title, author name(s), affiliation(s), mailing, and email address(es) should begin each Abstract. An accompanying cover letter should include: Full title of Abstract; corresponding author and presenting author name, address, telephone, fax, and email address; requested preference for oral or poster presentation; and a first and second choice from the topics above, including whether it is biological (B) or technological (T) work. Example: first choice: vision (T); second choice: neural system models (B). (Talks will be 15 minutes long. Posters will be up for a full day. Overhead, slide, VCR, and LCD projector facilities will be available for talks.) Abstracts which do not meet these requirements or which are submitted with insufficient funds will be returned. Accepted Abstracts will be printed in the conference proceedings volume. No longer paper will be required. The original and 3 copies of each Abstract should be sent to: Cynthia Bradford, Boston University, Department of Cognitive and Neural Systems, 677 Beacon Street, Boston, MA 02215. REGISTRATION INFORMATION: Early registration is recommended. To register, please fill out the registration form below. Student registrations must be accompanied by a letter of verification from a department chairperson or faculty/research advisor. If accompanied by an Abstract or if paying by check, mail to the address above. If paying by credit card, mail as above, or fax to (617) 353-7755, or email to cindy at cns.bu.edu. The registration fee will help to pay for a reception, 6 coffee breaks, and the meeting proceedings. STUDENT FELLOWSHIPS: Fellowships for PhD candidates and postdoctoral fellows are available to help cover meeting travel and living costs. The deadline to apply for fellowship support is January 31, 2002. Applicants will be notified by email by February 28, 2002. Each application should include the applicant's CV, including name; mailing address; email address; current student status; faculty or PhD research advisor's name, address, and email address; relevant courses and other educational data; and a list of research articles. A letter from the listed faculty or PhD advisor on official institutional stationery should accompany the application and summarize how the candidate may benefit from the meeting. Fellowship applicants who also submit an Abstract need to include the registration fee with their Abstract submission. Those who are awarded fellowships are required to register for and attend both the conference and the day of tutorials. Fellowship checks will be distributed after the meeting. REGISTRATION FORM Sixth International Conference on Cognitive and Neural Systems Department of Cognitive and Neural Systems Boston University 677 Beacon Street Boston, Massachusetts 02215 Tutorials: May 29, 2002 Meeting: May 30 - June 1, 2002 FAX: (617) 353-7755 http://www.cns.bu.edu/meetings/ (Please Type or Print) Mr/Ms/Dr/Prof: _____________________________________________________ Name: ______________________________________________________________ Affiliation: _______________________________________________________ Address: ___________________________________________________________ City, State, Postal Code: __________________________________________ Phone and Fax: _____________________________________________________ Email: _____________________________________________________________ The conference registration fee includes the meeting program, reception, two coffee breaks each day, and meeting proceedings. The tutorial registration fee includes tutorial notes and two coffee breaks. CHECK ONE: ( ) $85 Conference plus Tutorial (Regular) ( ) $55 Conference plus Tutorial (Student) ( ) $60 Conference Only (Regular) ( ) $40 Conference Only (Student) ( ) $25 Tutorial Only (Regular) ( ) $15 Tutorial Only (Student) METHOD OF PAYMENT (please fax or mail): [ ] Enclosed is a check made payable to "Boston University". Checks must be made payable in US dollars and issued by a US correspondent bank. Each registrant is responsible for any and all bank charges. [ ] I wish to pay my fees by credit card (MasterCard, Visa, or Discover Card only). Name as it appears on the card: _____________________________________ Type of card: _______________________________________________________ Account number: _____________________________________________________ Expiration date: ____________________________________________________ Signature: __________________________________________________________ From jzhu at stanford.edu Fri Aug 24 12:35:24 2001 From: jzhu at stanford.edu (Ji Zhu) Date: Fri, 24 Aug 2001 09:35:24 -0700 (PDT) Subject: No subject Message-ID: Dear all, This is a repost of our paper "Kernel Logistic Regression and the Import Vector Machine". We want to apologize that we missed several important references in our previous draft. The revised version is available at http://www.stanford.edu/~jzhu/research/nips01.ps Thank you! Best regards, -Ji Zhu From skremer at q.cis.uoguelph.ca Mon Aug 27 16:29:04 2001 From: skremer at q.cis.uoguelph.ca (Stefan C. Kremer) Date: Mon, 27 Aug 2001 16:29:04 -0400 (EDT) Subject: Announce: New Unlabeled Data Competition and Workshop Message-ID: Apologies if you receive multiple copies of this mailing. ANNOUNCEMENT: The Second Annual NIPS Unlabeled Data Competition and Workshop It's time to put-up or shut-up! Synopsis: We are please to announce the NIPS*2001 Unlabeled Data Competition and Workshop, to be held in Whistler, British Columbia, Canada, Dec 7 or 8, 2001. This competition is a challenge to the machine learning community to develop and demonstrate methods to use unlabeled data to improve supervised learning. We have created a web-site where participants can download and submit problem sets and compete head to head with other contestants in a series of challenging unlabeled-data, supervised-learning problems. Recently, there has been much interest in applying techniques that incorporate knowledge from unlabeled data into systems performing supervised learning. The potential advantages of such techniques are obvious in domains where labeled data is expensive and unlabeled data is cheap. Many such techniques have been proposed, but only recently has any effort been made to compare the effectiveness of different approaches on real world problems. Our contest presents a challenge to the proponents of methods to incorporate unlabeled data into supervised learning. Can you really use unlabeled data to help train a supervised classification (or regression) system? Do recent (and not so recent) theories stand up to the data test? On the contest web-site you can find challenge problems where you can try out your methods head-to-head against anyone brave enough to face you. Then, at the end of the contest we will release the results and find out who really knows something about using unlabeled data, and if unlabeled data are really useful or we are all just wasting our time. So ask yourself, are you (and your theory) up to the challenge?? Feeling lucky??? For more details on the competition or the workshop and to sign up for the Unlabeled Data Mailing List, please visit our web-page at "http://q.cis.uoguelph.ca/~skremer/NIPS2001/". Stefan -- -- Dr. Stefan C. Kremer, Assistant Prof., Dept. of Computing and Information Science University of Guelph, Guelph, Ontario N1G 2W1 WWW: http://hebb.cis.uoguelph.ca/~skremer Tel: (519)824-4120 Ext.8913 Fax: (519)837-0323 E-mail: skremer at snowhite.cis.uoguelph.ca From bbs at bbsonline.org Tue Aug 28 16:55:50 2001 From: bbs at bbsonline.org (Stevan Harnad - Behavioral & Brain Sciences (Editor)) Date: Tue, 28 Aug 2001 16:55:50 -0400 Subject: BBS Call for Commentators--Preston & De Waal: Empathy: Its ultimate and proximate bases Message-ID: Dear Dr. Connectionists List User, Below is the abstract of a forthcoming BBS target article Empathy: Its ultimate and proximate bases by Stephanie D. Preston & Frans B. M. de Waal http://www.bbsonline.org/Preprints/Preston/ or http://www.bbsonline.org/Preprints/Preston/Preston.pdf This article has been accepted for publication in Behavioral and Brain Sciences (BBS), an international, interdisciplinary journal providing Open Peer Commentary on important and controversial current research in the biobehavioral and cognitive sciences. Commentators must be BBS Associates or nominated by a BBS Associate. To be considered as a commentator for this article, to suggest other appropriate commentators, or for information about how to become a BBS Associate, please reply by EMAIL within three (3) weeks to: calls at bbsonline.org The Calls are sent to 10,000 BBS Associates, so there is no expectation (indeed, it would be calamitous) that each recipient should comment on every occasion! Hence there is no need to reply except if you wish to comment, or to nominate someone to comment. If you are not a BBS Associate, please approach a current BBS Associate (there are currently over 10,000 worldwide) who is familiar with your work to nominate you. All past BBS authors, referees and commentators are eligible to become BBS Associates. A full electronic list of current BBS Associates is available at this location to help you select a name: http://www.bbsonline.org/Instructions/assoclist.html If no current BBS Associate knows your work, please send us your Curriculum Vitae and BBS will circulate it to appropriate Associates to ask whether they would be prepared to nominate you. (In the meantime, your name, address and email address will be entered into our database as an unaffiliated investigator.) To help us put together a balanced list of commentators, please give some indication of the aspects of the topic on which you would bring your areas of expertise to bear if you were selected as a commentator. To help you decide whether you would be an appropriate commentator for this article, an electronic draft is retrievable from the online BBSPrints Archive, at the URL that follows the abstract below. _____________________________________________________________ Empathy: Its ultimate and proximate bases Stephanie D. Preston Department of Psychology 3210 Tolman Hall #1650 University of California at Berkeley Berkeley, CA 94720-1650 USA spreston at socrates.berkeley.edu http://socrates.berkeley.edu/~spreston Frans B. M. de Waal Living Links, Yerkes Primate Center and Psychology Department, Emory University, Atlanta, GA 30322 USA dewaal at rmy.emory.edu http://www.emory.edu/LIVING_LINKS/ KEYWORDS: altruism; cognitive empathy; comparative; emotion; emotional contagion; empathy; evolution; human; perception-action; perspective taking; ABSTRACT: There is disagreement in the literature about the exact nature of the phenomenon of empathy. There are emotional, cognitive, and conditioning views, applying in varying degrees across species. An adequate description of the ultimate and proximate mechanism can integrate these views. Proximately, the perception of an object's state activates the subject's corresponding representations, which in turn activate somatic and autonomic responses. This mechanism supports basic behaviors (e.g., alarm, social facilitation, vicariousness of emotions, mother-infant responsiveness, and the modeling of competitors and predators) that are crucial for the reproductive success of animals living in groups. The "Perception-Action Model" (PAM) together with an understanding of how representations change with experience can explain the major empirical effects in the literature (similarity, familiarity, past experience, explicit teaching and salience). It can also predict a variety of empathy disorders. The interaction between the PAM and prefrontal functioning can also explain different levels of empathy across species and age groups. This view can advance our evolutionary understanding of empathy beyond inclusive fitness and reciprocal altruism and can explain different levels of empathy across individuals, species, stages of development, and situations. http://www.bbsonline.org/Preprints/Preston/ or http://www.bbsonline.org/Preprints/Preston/Preston.pdf ___________________________________________________________ Please do not prepare a commentary yet. Just let us know, after having inspected it, what relevant expertise you feel you would bring to bear on what aspect of the article. We will then let you know whether it was possible to include your name on the final formal list of invitees. _______________________________________________________________________ *** SUPPLEMENTARY ANNOUNCEMENTS *** (1) The authors of scientific articles are not paid money for their refereed research papers; they give them away. What they want is to reach all interested researchers worldwide, so as to maximize the potential research impact of their findings. Subscription/Site-License/Pay-Per-View costs are accordingly access-barriers, and hence impact-barriers for this give-away research literature. There is now a way to free the entire refereed journal literature, for everyone, everywhere, immediately, by mounting interoperable university eprint archives, and self-archiving all refereed research papers in them. Please see: http://www.eprints.org http://www.openarchives.org/ http://www.dlib.org/dlib/december99/12harnad.html --------------------------------------------------------------------- (2) All authors in the biobehavioral and cognitive sciences are strongly encouraged to self-archive all their papers in their own institution's Eprint Archives or in CogPrints, the Eprint Archive for the biobehavioral and cognitive sciences: http://cogprints.soton.ac.uk/ It is extremely simple to self-archive and will make all of our papers available to all of us everywhere, at no cost to anyone, forever. Authors of BBS papers wishing to archive their already published BBS Target Articles should submit it to BBSPrints Archive. Information about the archiving of BBS' entire backcatalogue will be sent to you in the near future. Meantime please see: http://www.bbsonline.org/help/ and http://www.bbsonline.org/Instructions/ --------------------------------------------------------------------- (3) Call for Book Nominations for BBS Multiple Book Review In the past, Behavioral and Brain Sciences (BBS) had only been able to do 1-2 BBS multiple book treatments per year, because of our limited annual page quota. BBS's new expanded page quota will make it possible for us to increase the number of books we treat per year, so this is an excellent time for BBS Associates and biobehavioral/cognitive scientists in general to nominate books you would like to see accorded BBS multiple book review. (Authors may self-nominate, but books can only be selected on the basis of multiple nominations.) It would be very helpful if you indicated in what way a BBS Multiple Book Review of the book(s) you nominate would be useful to the field (and of course a rich list of potential reviewers would be the best evidence of its potential impact!). *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* Please note: Your email address has been added to our user database for Calls for Commentators, the reason you received this email. If you do not wish to receive further Calls, please feel free to change your mailshot status through your User Login link on the BBSPrints homepage, useing your username and password above: http://www.bbsonline.org/ For information about the mailshot, please see the help file at: http://www.bbsonline.org/help/node5.html#mailshot *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* From brody at cshl.org Tue Aug 28 18:35:49 2001 From: brody at cshl.org (Carlos Brody) Date: Tue, 28 Aug 2001 18:35:49 -0400 (EDT) Subject: Postdoctoral positions in computational neuroscience Message-ID: <15244.7365.990066.563845@sonnabend.cshl.org> -- PLEASE POST -- POSTDOCTORAL OPPORTUNITIES IN COMPUTATIONAL NEUROSCIENCE Postdoctoral positions for computational neuroscientists and psychophysicists are available in Carlos Brody's research group at Cold Spring Harbor Laboratory. (see http://www.cns.caltech.edu/~carlos/temporary/Lab). Applicants should have an interest in quantitative approaches to neuroscience, and should have, or be near completing, a Ph.D. in Neuroscience, Experimental Psychology, or in a quantitative field (e.g. Physics, Math, Engineering). Successful applicants will be expected, after appropriate guidance and/or any necessary self-education, to lead the group's research efforts in one or more of the projects listed below. For more information on each of these projects, visit the lab's web page. In addition, those who wish to develop and pursue their own, independent, self-originated, line(s) of research will be very much encouraged to do so: the lab seeks an atmosphere of vigorous discussion and creative independence. Applications from self-guided, motivated, and independent-minded scientists are particularly welcome. Applicants should send a CV, the names of three references, and a summary of research interests and experience to: Carlos Brody, 1 Bungtown Road, Freeman Building, Cold Spring Harbor, NY 11724, USA. The positions are open immediately; salaries are on the NIH pay scale. ---------- Lab interest areas (in order of descending current emphasis in the lab): 1) Psychophysics and neurocomputational modeling of working memory. 2) Encoding and representation of time. 3) Computation with spiking neurons. 4) Automated mapping of complex receptive fields. From engp9286 at nus.edu.sg Wed Aug 29 03:39:47 2001 From: engp9286 at nus.edu.sg (Duan Kaibo) Date: Wed, 29 Aug 2001 15:39:47 +0800 Subject: a technical report Message-ID: <9C4C56CDF89E0440A6BD571E76D2387FB7559B@exs23.ex.nus.edu.sg> Dear Connectionists: We have recently completed a technical report that evaluates some simple performance measures for tuning hyperparameters of Support Vector Machines. A pdf file containing this report can be downloaded from: http://guppy.mpe.nus.edu.sg/~mpessk/comparison.shtml Here are the details of the report... __________________________________________________________________ Title: Evaluation of Simple Performance Measures for Tuning SVM Hyperparameters Authors: Kaibo Duan ( engp9286 at nus.edu.sg ) S. Sathiya Keerthi ( mpessk at nus.edu.sg ) Aun Neow Poo ( mpepooan at nus.edu.sg ) Abstract: Choosing optimal hyperparameter values for support vector machines is an important step in SVM design. This is usually done by minimizing either an estimate of generalization error or some other related performance measure. In this paper, we empirically study the usefulness of several simple performance measures that are inexpensive to compute (in the sense that they do not require expensive matrix operations involving the kernel matrix). The results point out which of these measures are adequate functionals for tuning SVM hyperparameters. For SVMs with L1 soft margin formulation, none of the simple measures yields a performance uniformly as good as k-fold cross validation; Joachims' Xi-Alpha bound and Wahba et al's GACV come next and perform reasonably well. For SVMs with L2 soft margin formulation, the radius margin bound gives a very good prediction of optimal hyperparameter values. __________________________________________________________________ We are interested in knowing about the comparitive performance of the measures that we have considered, on other data sets that we haven't tried. Best regards, Kaibo From mike at stats.gla.ac.uk Wed Aug 29 10:17:12 2001 From: mike at stats.gla.ac.uk (Mike Titterington) Date: Wed, 29 Aug 2001 15:17:12 +0100 (BST) Subject: Postdoctoral post in Glasgow Message-ID: (Re-advertisement) UNIVERSITY OF GLASGOW DEPARTMENT OF STATISTICS POSTDOCTORAL RESEARCH ASSISTANT Applications are invited for a Postdoctoral Research Assistantship (IA) post in the Department of Statistics, University of Glasgow, to work with Professor D.M. Titterington for a period of up to 3 years, starting as soon as possible. The post is funded by the UK Engineering and Physical Sciences Research Council. The research topic is 'Approximate Approaches to Likelihood and Bayesian Statistical Inference in Incomplete-data problems'. Applications, supported by full curriculum vitae and the names of three referees, should be sent, to arrive no later than September 21, 2001, to Professor D. M. Titterington, Department of Statistics, University of Glasgow, Glasgow G12 8QQ, Scotland, from whom further particulars are available. Informal enquiries by electronic mail (mike at stats.gla.ac.uk) are welcomed. From juergen at idsia.ch Thu Aug 30 10:57:29 2001 From: juergen at idsia.ch (juergen@idsia.ch) Date: Thu, 30 Aug 2001 16:57:29 +0200 Subject: metalearner Message-ID: <200108301457.QAA01240@ruebe.idsia.ch> I would like to draw your attention to Sepp Hochreiter's astonishing recent result on "learning to learn." He trains gradient-based "Long Short-Term Memory" (LSTM) recurrent networks with roughly 5000 weights to _metalearn_ fast online learning algorithms for nontrivial classes of functions, such as all quadratic functions of two variables. LSTM is necessary because metalearning typically involves huge time lags between important events, and standard gradient-based recurrent nets cannot deal with these. After a month of metalearning on a PC he freezes all weights, then uses the frozen net as follows: He selects some new function f, and feeds a sequence of random training exemplars of the form ...data/target/data/target/data... into the input units, one sequence element at a time. After about 30 exemplars the frozen recurrent net correctly predicts target inputs before it sees them. No weight changes! How is this possible? After metalearning the frozen net implements a sequential learning algorithm which apparently computes something like error signals from data inputs and target inputs and translates them into changes of internal estimates of f. Parameters of f, errors, temporary variables, counters, computations of f and of parameter updates are all somehow represented in form of circulating activations. Remarkably, the new - and quite opaque - online learning algorithm running on the frozen network is much faster than standard backprop with optimal learning rate. This indicates that one can use gradient descent to metalearn learning algorithms that outperform gradient descent. Furthermore, the metalearning procedure automatically avoids overfitting in a principled way, since it punishes overfitting online learners just like it punishes slow ones, simply because overfitters and slow learners cause more cumulative errors during metalearning. Hochreiter himself admits the paper is not well-written. But the results are quite amazing: http://www.cs.colorado.edu/~hochreit @inproceedings{Hochreiter:01meta, author = "S. Hochreiter and A. S. Younger and P. R. Conwell", title = "Learning to learn using gradient descent", booktitle= "Lecture Notes on Comp. Sci. 2130, Proc. Intl. Conf. on Artificial Neural Networks (ICANN-2001)", editors = "G. Dorffner and H. Bischof and K. Hornik", publisher= "Springer: Berlin, Heidelberg", pages = "87-94", year = "2001"} ------------------------------------------------- Juergen Schmidhuber director IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland juergen at idsia.ch www.idsia.ch/~juergen From tibs at stat.Stanford.EDU Wed Aug 1 01:14:22 2001 From: tibs at stat.Stanford.EDU (Rob Tibshirani) Date: Tue, 31 Jul 2001 22:14:22 -0700 (PDT) Subject: book announcement Message-ID: <200108010514.WAA744941@rgmiller.Stanford.EDU> Book announcement: The Elements of Statistical Learning- data mining, inference and prediction 536p (in full color) Trevor Hastie, Robert Tibshirani, and Jerome Fridman Springer-Verlag, 2001 For full details see http://www-stat.stanford.edu/ElemStatLearn Here is a brief description: During the past decade there has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of Statistics, and spawned new areas such as data mining, machine learning and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data-mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting --- the first comprehensive treatment of this topic in any book. Jerome Friedman, Trevor Hastie, and Robert Tibshirani are Professors of Statistics at Stanford University. They are prominent researchers in this area: Friedman is the (co-)inventor of many data-mining tools including CART, MARS, and projection pursuit. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modelling software in S-PLUS, and invented principal curves and surfaces. Tibshirani proposed the Lasso and co-wrote the best selling book ``An Introduction to the Bootstrap''. ********************************************** Rob Tibshirani, Dept of Health Research & Policy and Dept of Statistics HRP Redwood Bldg Stanford University Stanford, California 94305-5405 phone: HRP: 650-723-7264 (Voice mail), Statistics 650-723-1185 FAX 650-725-8977 tibs at stat.stanford.edu http://www-stat.stanford.edu/~tibs From ingber at ingber.com Thu Aug 2 17:59:56 2001 From: ingber at ingber.com (Lester Ingber) Date: Thu, 2 Aug 2001 16:59:56 -0500 Subject: Paper: Probability tree algorithm for general diffusion processes Message-ID: <20010802165956.A13979@ingber.com> The following preprint is available: %A L. Ingber %A C. Chen %A R.P. Mondescu %A D. Muzzall %A M. Renedo %T Probability tree algorithm for general diffusion processes %J Physical Review E %P (to be published) %D 2001 %O URL http://www.ingber.com/path01_pathtree.ps.gz ABSTRACT Motivated by path-integral numerical solutions of diffusion processes, PATHINT, we present a new tree algorithm, PATHTREE, which permits extremely fast accurate computation of probability distributions of a large class of general nonlinear diffusion processes. -- Prof. Lester Ingber ingber at ingber.com www.ingber.com ingber at alumni.caltech.edu www.alumni.caltech.edu/~ingber From allan at biomedica.org Thu Aug 2 18:24:25 2001 From: allan at biomedica.org (Allan Kardec Barros) Date: Thu, 02 Aug 2001 19:24:25 -0300 Subject: Extraction of Specific Signals with Temporal Structure Message-ID: <3B69D319.562840E6@biomedica.org> Apologies if you receive multiple copies of this message. Dear Everyone, I would like to announce the following paper, recently published in Neural Computation. For those familiar with ICA, the difference in this algorithm is basically that, given some simple assumptions, we prove that the permutation problem can be avoided, while the algorithm is quite simple and based on second order statistics, which does not require that at most one signal to be Gaussian. Please feel free to mail me requesting either PS or PDF copies of our work. Best Regards, ak. TITLE: Extraction of Specific Signals with Temporal Structure. AUTORS: A. K. Barros and A. Cichocki. ABSTRACT: In this work we develop a very simple batch learning algorithm for semi-blind extraction of a desired source signal with temporal structure from linear mixtures. Although we use the concept of sequential blind extraction of sources and independent component analysis (ICA), we do not carry out the extraction in a completely blind manner neither we assume that sources are statistically independent. In fact, we show that the {\it a priori} information about the auto-correlation function of primary sources can be used to extract the desired signals (sources of interest) from their linear mixtures. Extensive computer simulations and real data application experiments confirm the validity and high performance of the proposed algorithm. From ted.carnevale at yale.edu Fri Aug 3 12:07:49 2001 From: ted.carnevale at yale.edu (Ted Carnevale) Date: Fri, 03 Aug 2001 12:07:49 -0400 Subject: NEURON course at SFN 2001 meeting Message-ID: <3B6ACC55.93EBBAD1@yale.edu> Short Course Announcement USING THE NEURON SIMULATION ENVIRONMENT Satellite Symposium, Society for Neuroscience Meeting 9 AM - 5 PM on Saturday, Nov. 10, 2001 Speakers: N.T. Carnevale, M.L. Hines, J.W. Moore, and G.M. Shepherd This 1 day course with lectures and live demonstrations will present information essential for teaching and research applications of NEURON, an advanced simulation environment that handles realistic models of biophysical mechanisms, individual neurons, and networks of cells. The emphasis is on practical issues that are key to the most productive use of this powerful and convenient modeling tool. Features that will be covered include: constructing and managing models with the CellBuilder, Network Builder, and Linear Circuit Builder importing detailed morphometric data using the Multiple Run Fitter to optimize models with high-dimensional parameter spaces database resources for empirically-based modeling Each registrant will a comprehensive set of notes which include material that has not appeared elsewhere in print. For more information see the course's WWW pages at http://www.neuron.yale.edu/sd2001.html --Ted Supported in part by the National Science Foundation. Opinions expressed are those of the authors and not necessarily those of the Foundation. From Alton.Ford at tmp.com Fri Aug 3 18:14:06 2001 From: Alton.Ford at tmp.com (Ford, Alton) Date: Fri, 3 Aug 2001 17:14:06 -0500 Subject: Postdoc positions at Los Alamos National Laboratory Message-ID: Postdoctoral Positions in Experimental and Computational Neuroscience The Biophysics Group (http://www.biophysics.lanl.gov/) in the Physics Division at Los Alamos National Laboratory seeks several postdoctoral candidates in the areas of experimental and computational neuroscience. Existing projects include recording of fast optical transients from neural tissue and the development of associated high speed data acquisition systems, imaging devices and optical technology, analysis of evoked MEG and fMRI signals, computational modeling of information processing within the biological neural systems, and collaborative work on the development of a retinal prosthetic device. Successful candidates could combine work in several of these areas. For further technical information, contact Dr. John George at jsg at lanl.gov. A Ph.D. in Physics, Electrical Engineering, Biology, or a related discipline completed within the last three years or soon to be completed is required. Current starting salaries range from $54,100 - $58,300. Further details about the Postdoctoral Program may be found at: http://www.hr.lanl.gov/postdoc/. For consideration, submit a resume and publications list with a cover letter outlining current research interests, including contact information for three references, to postdoc-jobs at lanl.gov (reference PD017639), or submit two copies to: Postdoc Program Office, PD017639, MS-P290, Los Alamos National Laboratory, Los Alamos, NM 87545. Los Alamos National Laboratory is operated by the University of California for the U.S. Department of Energy. AA/EOE Alton Ford Account Executive tmp.worldwide Advertising & Communications 3032 Bunker Hill Lane, Suite 207 Santa Clara, CA 95054 * 408.844.0150 * 408.496.6704 fax * alton.ford at tmp.com www.tmp.com Compliment your recruitment advertising with Web Dragon! ...A service provided by TMP Worldwide, in which a team of our live professionals mine through the millions of resumes on the Internet to find qualified resumes to meet your specified recruitment needs. There are more resumes online than ever. Fill your pipeline with quality resumes and let TMP do the work for you! Please contact me for more information. From juergen at idsia.ch Mon Aug 6 12:20:15 2001 From: juergen at idsia.ch (juergen@idsia.ch) Date: Mon, 6 Aug 2001 18:20:15 +0200 Subject: PhD fellowship Message-ID: <200108061620.SAA08768@ruebe.idsia.ch> I am seeking a PhD student for research on state-of-the-art recurrent neural networks. Please see http://www.idsia.ch/~juergen/phd2001.html Interviews also possible at ICANN 2001 (Aug 21-25) in Vienna or at the ICANN recurrent net workshop: http://www.idsia.ch/~doug/icann/index.html ------------------------------------------------- Juergen Schmidhuber director IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland juergen at idsia.ch www.idsia.ch/~juergen From CogSci at psyvax.psy.utexas.edu Tue Aug 7 13:34:51 2001 From: CogSci at psyvax.psy.utexas.edu (Cognitive Science Society) Date: Tue, 07 Aug 2001 12:34:51 -0500 Subject: Richard M. Shiffrin awarded the Rumelhart Prize Message-ID: <5.0.0.25.2.20010807123359.00b05848@psy.utexas.edu> Richard M. Shiffrin Chosen to Receive the David E. Rumelhart Prize for Contributions to the Formal Analysis of Human Cognition The Glushko-Samuelson Foundation and the Cognitive Science Society are pleased to announce that Richard M. Shiffrin has been chosen as the second recipient of the $100,000 David E. Rumelhart Prize, awarded annually for an outstanding contribution to the formal analysis of human cognition. Shiffrin will receive this prize and give the Prize Lecture at the 2002 Meeting of the Cognitive Science Society, at George Mason University, August 7-11, 2002. Shiffrin has made many contributions to the modeling of human cognition in areas ranging from perception to attention to learning, but is best known for his long-standing efforts to develop explicit models of human memory. His most recent models use Bayesian, adaptive approaches, building on previous work but extending it in a critical new manner, and carrying his theory beyond explicit memory to implicit learning and memory processes. The theory has been evolving for about 35 years, and as a result represents a progression similar to the best theories seen in any branch of science. Shiffrin's major effort began in 1968, in a chapter with Atkinson [1] that laid out a model of the components of short- and long-term memory and described the processes that control the operations of memory. The Atkinson-Shiffrin model encapsulated empirical and theoretical results from a very large number of publications that modeled quantitatively the relation of short- to long-term memory. It achieved its greatest success by showing the critical importance---and the possibility---of modeling the control processes of cognition. This chapter remains one of the most cited works in the entire field of psychology. Shiffrin's formal theory was taken forward in a quantum leap in 1980 [2] and 1981 [3] with the SAM (Search of Associative Memory) model. This was a joint effort with Jeroen Raaijmakers, then a graduate student. The SAM model quantified the nature of retrieval from long-term memory, and characterized reCALL as a memory search with cycles of sampling and recovery. The SAM theory precisely incorporates the notions of interactive cue combination that are now seen to lie at the heart of memory retrieval. Another major quantum step occurred in 1984 [4] when the theory was extended to recognition memory. With another former student, Gary Gillund, Shiffrin initiated what has become the standard approach to recognition memory, in which a decision is based on summed activation of related memory traces. It was a major accomplishment that the same retrieval activations that had been used in the recall model could be carried forward and used to predict a wide range of recognition phenomena. The next major step occurred in 1990, when Shiffrin published two articles on the list-length effect with his student Steve Clark and his colleague, Roger Ratcliff [5, 6]. This research was of critical importance in that it established clearly that experience leads to the differentiation, rather than the mere stregthening, of the representations of items in memory. In 1997, the theory evolved in a radical direction in an important paper with another former student, Mark Steyvers [7]. Although the changes were fundamental, the new model retained the best concepts of its predecessors, so that the previous successful predictions were also a part of the new theory. REM added featural representations, to capture similarity relations among items in memory. Building on earlier ideas by John Anderson, and related ideas developed in parallel by McClelland and Chappell, Shiffrin used Bayesian principles of adaptive and optimal decision making under constraints to guide the selection of the quantitative form of the activation functions. In addition, storage principles were set forth that provided mechanisms by which episodic experience could coalesce over development and experience into permanent non-contextualized knowledge. This latter development allowed the modeling of implicit memory phenomena, in work that is just now starting to appear in many journals, including a theory of long-term priming [with Schooler and Raaijmakers, 8] and a theory of short-term priming [with his student David Huber and others, 9]. The short-term priming research showed that the direction of priming can be reversed by extra study given to particular primes, leading to another conceptual breakthrough. A new version of the REM model explains this and other findings by assuming that some prime features are confused with test item features, and that the system attempts to deal with this situation optimally by appropriate discounting of evidence from certain features. Biographical Information Shiffrin received his Ph. D. from the Mathematical Psychology Program in the Department of Psychology at Stanford University in 1968, the year after Rumelhart received his degree from the same program. Since 1968 he has been on the faculty of the Department of Psychology at Indiana University, where he is now the Luther Dana Waterman Professor of Psychology and Director of the Cognitive Science Program. Shiffrin has accumulated many honors, including membership in the National Academy of Sciences, the American Academy of Arts and Sciences, the Howard Crosby Warren Award of the Society of Experimental Psychologists, and a MERIT Award from the National Institute of Mental Health. Shiffrin has served the field as editor of the Journal of Experimental Psychology: Learning Memory and Cognition, and as a member of the governing boards of several scientific societies. Cited Articles By Richard M. Shiffrin [1] Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence and J. T. Spence (Eds.), The Psychology of Learning and Motivation: Advances in Research and Theory (Vol. 2, pp. 89-195). New York: Aaademic Press. [2] Raaijmakers, J. G. W., & Shiffrin, R. M. (1980). SAM: A theory of probabilistic search of associative memory. In Bower, G. H. (Ed.), The Psychology of Learning and Motivation, Vol. 14, 207-262. New York: Academic Press. [3] Raaijmakers, J. G. W., & Shiffrin, R. M. (1981). Search of associative memory. Psychological Review, 88, 93-134. [4] Gillund, G., & Shiffrin, R. M. (1984). A retrieval model for both recognition and recall. Psychological Reviw, 91, 1-67. [5] Ratcliff, R., Clark, S., & Shiffrin, R. M. (1990). The list-strength effect: I. Data and discussion. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 163-178. [6] Shiffrin, R. M., Ratcliff, R., & Clark, S. (1990). The list-strength effect: II. Theoretical mechanisms. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 179-195. [7] Shiffrin, R. M., & Steyvers, M. (1997). A model for recognition memory: REM: Retrieving effectively from memory. Psychonomic Bulletin and Review, 4 (2), 145-166. [8] Schooler, L., Shiffrin, R. M., & Raaijmakers, J. G. W. (2001). A model for implicit effects in perceptual identification. Psychological Review, 108, 257-272. [9] Huber, D. E., Shiffrin, R. M., Lyle, K. B., & Ruys, K. I. (2001). Perception and preference in short-term word priming. Psychological Review, 108, 149-182. ================================================================ Geoffrey E. Hinton Named First Recipient of the David E. Rumelhart Prize May 3, 2001 Today the Glushko-Samuelson foundation and the Cognitive Science Society jointly announced that Geoffrey E. Hinton has been named the first recipient of the David E. Rumelhart Prize for contemporary contributions to the formal analysis of human cognition. Hinton, the Director of the Gatsby Computational Neuroscience Unit at University College, London, was chosen from a large field of outstanding nominees because of his seminal contributions to the understanding of neural networks. "Hinton's insights into the analysis of neural networks played a central role in launching the field in the mid-1980's" said Professor James McClelland of Carnegie Mellon University, Chair of the Prize Selection Committee, "Geoff also played a major role in conveying the relevance of neural networks to higher-level cognition." Professor Lawrence Barsalou of Emory University, President of the Cognitive Science Society, agreed with this assessment. "Hinton's contributions to Cognitive Science have been pivotal", said Barsalou. "As the first recipient he sets a great example for future awards." Hinton will receive the prize, which includes a monetary award of $100,000, at the annual meeting of the Society in Edinburgh, Scotland, in early August, 2001. The Rumelhart prize acknowledges intellectual generosity and effective mentoring as well as scientific insight. "Dave Rumelhart gave away many scientific ideas, and made important contributions to the work of many of his students and co-workers" said Robert J. Glushko, President of the Glushko-Samuelson foundation. He added "Hinton stands out not only for his own contributions but for his exemplary record in mentoring young scientists." A total of eighteen graduate students have received their Ph. D.'s under Hinton's supervision. In conjunction with naming Hinton as the first recipient of the David E. Rumelhart Prize, the Glushko-Samuelson foundation announced that the prize will be awarded on an annual basis, instead of biennially. "This change reflects the number of outstanding scientists who were nominated for the award" noted Glushko. "I am pleased that my foundation can play a role in honoring their contributions to cognitive science." The second recipient of the Prize will be announced at the Edinburgh meeting of the society, and will give the prize lecture at the next annual meeting, which will be at George Mason University in August, 2002. For further information, please visit the David E. Rumelhart Prize web site: http://www.cnbc.cmu.edu/derprize/DerPrize2001.html or contact: Robert J. Glushko, 415-644-8731 James L. McClelland, 412-268-3157 ---------- Cognitive Science Society c/o Tanikqua Young Department of Psychology University of Texas Austin, TX 78712 Phone: (512) 471-2030 Fax: (512) 471-3053 ---------- From amari at brain.riken.go.jp Thu Aug 9 00:57:01 2001 From: amari at brain.riken.go.jp (Shun-ichi Amari) Date: Thu, 9 Aug 2001 13:57:01 +0900 Subject: FW: new book on Information Geometry Message-ID: ******************** ???? ?????????????????????????? ????????????????????????? ???????? ????????????????? Dear Connectionists I have announced the publication of the book "Methods of Information Geometry" but heard complaints that the book is out of stock. Now they printed further, and you can order from AMS or Oxford University Press through bookshops. ************* It is my pleasure to announce the publication of a book on Information Geometry. I have been often asked if there is a good book on information geometry to know its general perspectives. Here it is. S.Amari and H.Nagaoka, Methods of Information Geometry, AMS Translations of Mathematical Monographs, vol 191 (translated by Daishi Harada) American Mathematical Society (AMS) and Oxford University Press, 206 + x pages, 2000. (See http://www.ams.org/) ******************** Shun-ichi Amari Vice Director, RIKEN Brain Science Institute Laboratory for Mathematical Neuroscience Research Group on Brain-Style Information Systems tel: +81-(0)48-467-9669; fax: +81-(0)48-467-9687 amari at brain.riken.go.jp http://www.bsis.brain.riken.go.jp/ From orhan at ait-tech.com Wed Aug 8 14:16:02 2001 From: orhan at ait-tech.com (Orhan Karaali) Date: Wed, 8 Aug 2001 14:16:02 -0400 Subject: Research Scientist position at Advanced Investment Technology Message-ID: ADVANCED INVESTMENT TECHNOLOGY, INC. www.ait-tech.com Advanced Investment Technology, Inc. (AIT) is a registered investment advisor based in Clearwater, Florida focusing on institutional domestic equity asset management. Our partners include Boston-based State Street Global Advisors, a global leader in institutional financial services, and Amsterdam-based Stichting Pensioenfonds ABP, one of the world's largest pension plans. AIT's reputation as an innovative entrepreneur within the asset management community is built upon the research and development of nontraditional quantitative stock valuation techniques (neural networks and genetic algorithms) for which a patent was issued in 1998. POSITION: RESEARCH SCIENTIST The position will involve developing software and valuation algorithms for stock selection and portfolio management. Job responsibilities include database development, running weekly production jobs, working with financial data vendor feeds, contributing to financial research projects, and developing applications in the areas of multifactor stock models. AIT uses Windows 2000; Visual Studio 6 and Visual Studio Net; MS SQL 2000; C++ STL; OLE DB; XML; SOAP; and OLAP technologies. Minimum Qualifications: Bachelors Degree in Computer Science or a related field Masters Degree in Computer Science or MBA Very strong C++ and STL background Working knowledge of SQL Bonus Qualifications: Familiarity with financial data and asset management Experience developing object oriented software with C++ and STL Familiarity with Microsoft Visual Studio Knowledge of machine learning algorithms (NN, GA, GP, SVM) To apply, please send your resume to: E-mail: orhan at ait-tech.com Fax: (727) 799-1232 (Attn: Orhan Karaali) From norman at psych.colorado.edu Sat Aug 11 00:03:12 2001 From: norman at psych.colorado.edu (Ken Norman) Date: Fri, 10 Aug 2001 22:03:12 -0600 (MDT) Subject: new paper: modeling hippocampal and neocortical contributions to recognition memory Message-ID: Dear Connectionists, The following technical report is now available for downloading as: ftp://grey.colorado.edu/pub/oreilly/papers/NormanOReilly01_recmem.pdf webpage: http://psych.colorado.edu/~oreilly/pubs-abstr.html#01_recmem Modeling Hippocampal and Neocortical Contributions to Recognition Memory: A Complementary Learning Systems Approach Kenneth A. Norman and Randall C. O'Reilly Department of Psychology University of Colorado Boulder, CO 80309 ICS Technical Report 01-02 Abstract: We present a computational neural network model of recognition memory based on the biological structures of the hippocampus and medial temporal lobe cortex (MTLC), which perform complementary learning functions. The hippocampal component of the model contributes to recognition by recalling specific studied details. MTLC can not support recall, but it is possible to extract a scalar familiarity signal from MTLC that tracks how well the test item matches studied items. We present simulations that establish key qualitative differences in the operating characteristics of the hippocampal recall and MTLC familiarity signals, and we identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the two signals. We also use the model to address the stochastic relationship between recall and familiarity (i.e., are they independent), and the effects of partial vs. complete hippocampal lesions on recognition. From yokoy at brain.riken.go.jp Tue Aug 14 02:12:01 2001 From: yokoy at brain.riken.go.jp (Yoko Yamaguchi) Date: Tue, 14 Aug 2001 15:12:01 +0900 Subject: Postdoctorial/ technical staff positions in cognitive science and computational neurosicence Message-ID: Please post: POSTDOCTORAL SCIENTIST/ TECHNICAL STAFF POSITIONS at RIKEN BSI Laboratory for Dynamics of Emergent Intelligence, Brain-Style Intelligence Research Group, RIKEN Brain Science Institute (BSI) invites applicants for postdoctoral and technical staff scientists in the fields of cognitive science and computational neurosicence. Our objective is to clarify the neural principle for the dynamics of emergent intelligence in novel situations. Particular emphasis is given to synchronization of oscillations in hierarchical neural networks. For further information see http://www.dei.brain.riken.go.jp/ Applicants for postdoctoral positions must have a PhD. Technical staff are expected to have a bachelors or masters degree. Applicant should submit a full CV detailing education and experience, attached with your photograph, in addition to a complete bibliography of publications. The names, addresses, email addresses of two referees must also be supplied. Send all applications to: Contact:: Dr. Yoko yamaguchi (Laboratory Head) Lab. for Dynamics of Emergent Intelligence Brain Science Institute, RIKEN 2-1 Hirosawa, Wako, Saitama, 351-0198 Japan FAX: +81-48-467-6938 E-mail : yokoy at brain.riken.go.jp ------------------------------------------------------------------- Yoko Yamaguchi Lab. for Dynamics of Emergent Intelligence RIKEN Brain Science Institute(BSI) From bbs at bbsonline.org Mon Aug 13 16:27:04 2001 From: bbs at bbsonline.org (Stevan Harnad - Behavioral & Brain Sciences (Editor)) Date: Mon, 13 Aug 2001 16:27:04 -0400 Subject: Norman: Two Visual Systems -- BBS Call for Commentators Message-ID: Dear Dr. Connectionists List User, Below is the abstract of a forthcoming BBS target article Two Visual Systems and Two Theories of Perception: An Attempt to Reconcile the Constructivist and Ecological Approaches by Joel Norman http://www.bbsonline.org/Preprints/Norman/ http://psy.haifa.ac.il/~maga/tvs&ttp.pdf This article has been accepted for publication in Behavioral and Brain Sciences (BBS), an international, interdisciplinary journal providing Open Peer Commentary on important and controversial current research in the biobehavioral and cognitive sciences. Commentators must be BBS Associates or nominated by a BBS Associate. To be considered as a commentator for this article, to suggest other appropriate commentators, or for information about how to become a BBS Associate, please reply by EMAIL within three (3) weeks to: calls at bbsonline.org The Calls are sent to 8000 BBS Associates, so there is no expectation (indeed, it would be calamitous) that each recipient should comment on every occasion! Hence there is no need to reply except if you wish to comment, or to nominate someone to comment. If you are not a BBS Associate, please approach a current BBS Associate (there are currently over 10,000 worldwide) who is familiar with your work to nominate you. All past BBS authors, referees and commentators are eligible to become BBS Associates. A full electronic list of current BBS Associates is available at this location to help you select a name: http://www.bbsonline.org/Instructions/assoclist.html If no current BBS Associate knows your work, please send us your Curriculum Vitae and BBS will circulate it to appropriate Associates to ask whether they would be prepared to nominate you. (In the meantime, your name, address and email address will be entered into our database as an unaffiliated investigator.) To help us put together a balanced list of commentators, please give some indication of the aspects of the topic on which you would bring your areas of expertise to bear if you were selected as a commentator. To help you decide whether you would be an appropriate commentator for this article, an electronic draft is retrievable from the online BBSPrints Archive, at the URL that follows the abstract below. _____________________________________________________________ Two Visual Systems and Two Theories of Perception: An Attempt to Reconcile the Constructivist and Ecological Approaches Joel Norman Department of Psychology University of Haifa Haifa, Israel jnorman at psy.haifa.ac.il KEYWORDS: Visual perception theories, ecological, constructivist, two visual systems, space perception, size perception, dual-process approach ABSTRACT: The two contrasting theoretical approaches to visual perception, the constructivist and the ecological, are briefly presented and illustrated through their analyses of space perception and size perception. Earlier calls for their reconciliation and unification are reviewed. Neurophysiological, neuropsychological, and psychophysical evidence for the existence of two quite distinct visual systems, the ventral and the dorsal, is presented. These two perceptual systems differ in their functions; the ventral systems central function is that of identification, while the dorsal system is mainly engaged in the visual control of motor behavior. The strong parallels between the ecological approach and the functioning of the dorsal system and between the constructivist approach and the functioning of the ventral system are noted. It is also shown that the experimental paradigms used by the proponents of these two approaches match the functions of the respective visual systems. A dual-process approach to visual perception emerges from this analysis, with the ecological-dorsal process transpiring mainly without conscious awareness, while the constructivist-ventral process is normally conscious. Some implications of this dual-process approach to visual-perceptual phenomena are presented, with emphasis on space perception. http://www.bbsonline.org/Preprints/Norman/ http://psy.haifa.ac.il/~maga/tvs&ttp.pdf ___________________________________________________________ Please do not prepare a commentary yet. Just let us know, after having inspected it, what relevant expertise you feel you would bring to bear on what aspect of the article. We will then let you know whether it was possible to include your name on the final formal list of invitees. _______________________________________________________________________ *** SUPPLEMENTARY ANNOUNCEMENTS *** (1) The authors of scientific articles are not paid money for their refereed research papers; they give them away. What they want is to reach all interested researchers worldwide, so as to maximize the potential research impact of their findings. Subscription/Site-License/Pay-Per-View costs are accordingly access-barriers, and hence impact-barriers for this give-away research literature. There is now a way to free the entire refereed journal literature, for everyone, everywhere, immediately, by mounting interoperable university eprint archives, and self-archiving all refereed research papers in them. Please see: http://www.eprints.org http://www.openarchives.org/ http://www.dlib.org/dlib/december99/12harnad.html --------------------------------------------------------------------- (2) All authors in the biobehavioral and cognitive sciences are strongly encouraged to self-archive all their papers in their own institution's Eprint Archives or in CogPrints, the Eprint Archive for the biobehavioral and cognitive sciences: http://cogprints.soton.ac.uk/ It is extremely simple to self-archive and will make all of our papers available to all of us everywhere, at no cost to anyone, forever. Authors of BBS papers wishing to archive their already published BBS Target Articles should submit it to BBSPrints Archive. Information about the archiving of BBS' entire backcatalogue will be sent to you in the near future. Meantime please see: http://www.bbsonline.org/help/ and http://www.bbsonline.org/Instructions/ --------------------------------------------------------------------- (3) Call for Book Nominations for BBS Multiple Book Review In the past, Behavioral and Brain Sciences (BBS) had only been able to do 1-2 BBS multiple book treatments per year, because of our limited annual page quota. BBS's new expanded page quota will make it possible for us to increase the number of books we treat per year, so this is an excellent time for BBS Associates and biobehavioral/cognitive scientists in general to nominate books you would like to see accorded BBS multiple book review. (Authors may self-nominate, but books can only be selected on the basis of multiple nominations.) It would be very helpful if you indicated in what way a BBS Multiple Book Review of the book(s) you nominate would be useful to the field (and of course a rich list of potential reviewers would be the best evidence of its potential impact!). *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* Please note: Your email address has been added to our user database for Calls for Commentators, the reason you received this email. If you do not wish to receive further Calls, please feel free to change your mailshot status through your User Login link on the BBSPrints homepage. Check the helpfiles for details of how to obtain your username and password. http://www.bbsonline.org/ For information about the mailshot, please see the help file at: http://www.bbsonline.org/help/node5.html#mailshot *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* From murphyk at cs.berkeley.edu Tue Aug 14 20:42:21 2001 From: murphyk at cs.berkeley.edu (Kevin Murphy) Date: Tue, 14 Aug 2001 17:42:21 -0700 Subject: OpenBayes Message-ID: <3B79C56D.43E951A5@cs.berkeley.edu> Richard Dybowski formed the OpenBayes discussion group/email list on 17 January 2001. The goal is to discuss the development of an open source library for probabilistic graphical models. We had our first meeting at the recent UAI conference in Seattle. The only concrete decision reached was that we should advertise the existence of this group more widely - hence this email. For more details on the OpenBayes project, please see http://HTTP.CS.Berkeley.EDU/~murphyk/OpenBayes/index.html This page includes a list of people who attended the meeting, more details on the project's goals, achievements to date, ways you can subscribe to the list and/or contribute code, etc. Kevin Murphy P.S. If you have problems subscribing to the list, please send email to openbayes-owner at egroups.com, not to me! I am not the moderator. From neted at anc.ed.ac.uk Wed Aug 15 05:54:39 2001 From: neted at anc.ed.ac.uk (Network Editor) Date: Wed, 15 Aug 2001 10:54:39 +0100 Subject: NETWORK: Computation in Neural Systems Message-ID: <15226.18143.81476.664143@gargle.gargle.HOWL> Here is the contents page for the current issue of NETWORK: Computation in Neural Systems. NETWORK publishes original research work on theoretical and computational aspects of the development and functioning of the nervous system, at all levels of analysis, particularly at the network, cellular and subcellular levels. Professor David Willshaw Editor-in-Chief NETWORK: Computation in Neural Systems Institute for Adaptive & Neural Computation Division of Informatics University of Edinburgh 5 Forrest Hill Edinburgh EH1 2QL UK Tel: +44-(0)131-650 4404 Fax: +44-(0)131-650 4406 Email: neted at anc.ed.ac.uk ======================================================================== NETWORK: COMPUTATION IN NEURAL SYSTEMS - VOLUME 12, ISSUE 3, AUGUST 2001 Special issue featuring selected papers from the Natural Stimulus Statistics Workshop, October 2000, Cold Spring Harbor, USA EDITORIALS Publishing papers in Network: Special Issues D J Willshaw (p 235) Natural stimulus statistics P Reinagel and S Laughlin (pp 237-240) PAPERS Redundancy reduction revisited H Barlow (pp 241-253) Characterizing the sparseness of neural codes B Willmore and D J Tolhurst (pp 255-270) Beats, kurtosis and visual coding M G A Thomson (pp 271-287) Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli F E Theunissen, S V David, N C Singh, A Hsu, W E Vinje and J L Gallant (pp 289-316) Neural coding of naturalistic motion stimuli G D Lewen, W Bialek and R R de Ruyter van Steveninck (pp 317-329) Nonlinear and extra-classical receptive field properties and the statistics of natural scenes C Zetzsche and F R?hrbein (pp 331-350) Neuronal processing of behaviourally generated optic flow: experiments and model simulations R Kern, M Lutterklas, C Petereit, J P Lindemann and M Egelhaaf (pp 351-369) Can recent innovations in harmonic analysis `explain' key findings in natural image statistics? D L Donoho and A G Flesia (pp 371-393) Optimal nonlinear codes for the perception of natural colours T von der Twer and D I A MacLeod (pp 395-407) From colette.faucher at wanadoo.fr Thu Aug 16 05:40:32 2001 From: colette.faucher at wanadoo.fr (colette faucher) Date: Thu, 16 Aug 2001 02:40:32 -0700 Subject: cfp for FLAIRS special track : Categorization and Concept Representation : Models and Implications Message-ID: <3b7b16df3cc49870@amyris.wanadoo.fr> (added by amyris.wanadoo.fr) =========================================================================== FLAIRS 2002 15th International Florida Artificial Intelligence Research Society Conference Pensacola, Florida May 16-18, 2002 Special Track : "Categorization and Concept Representation : Models and Implications" =========================================================================== This track seeks to bring together researchers working on issues related to categorization and concept representation in the areas of Artificial Intelligence and Cognitive Psychology. Topic Description ------------------ Categorization is the process by which distinct entities are treated as equivalent. It is one of the most fundamental and pervasive cognitive activities. It is fundamental because categorization allows us to understand and make predictions about objects and events in our world. The problem of understanding what criteria are used to group together entities in a same category is indeed central in categorization. Though most works in that topic have proposed that perceptual or structural similarity is the "glue" that binds objects of a same category, some psychologists have claimed that similarity is insufficient to account for the acquisition and use of categories and have proposed more abstract forms of criteria that make categories coherent and give them a kind of homogeneity in terms of the entities that belong to them. The different new propositions psychologists have suggested are that objects are grouped together because they facilitate a common goal or serve the same function. Some categories are viewed as coherent because they rest on a theory which explains the commonalities of their elements. Similarity and goals, on one hand, and theories, on the other hand, have not been paid the same attention in computational models of categorization. Similarity-based models abound and the notion of categorization goals has also been exploited in computational models. On the other hand, the notion of an underlying theory that makes a category coherent just begins to be further analyzed and specified. New computational models of categorization reflecting this new tendency are thus expected. The representation of concepts that a categorization system generates is of course intimately tied to the criteria this system uses to group entities into categories, so along with new models of categorization, we expect to see the emergence of new models of concept representation apart from the classical ones deriving from the Aristotelician, the Prototypical and the Exemplar Views. The representation of the entities to categorize plays also an important part in the categorization process. In particular, the context in which the entities occur may influence the way they are classified. The purpose of this track is to bring fresh insights concerning a perhaps revisited notion of similarity, the way goals of categorization influence this process, how the notion of the theory of a concept can be formalized and implemented in computational models of categorization and the implications those elements may have on the representation of concepts. The contributions to this track may be situated in the symbolic approach of categorization or the connectionist one. Contributions in the following sub-topics would be welcomed : - Computational models of similarity, - Computational models of theory-based categorization, - Computational models of similarity-based categorization, - Computational models of human categorization, - Models of concept representation which are relevant as regards to the process of categorization, - Models of concept representation and elicitation, - Formalization of the notion of theory which underlies a category, - Formalization of the context of occurrence of the entities to categorize and its influence on the categorization process. This list is not exclusive provided that the contributions are relevant to the definition of the track specified above. Paper Review and Publication ------------------------------ Only full papers will be considered for the track. Submitted papers will be reviewed by two program committee members. An author for an accepted paper is expected to present the paper in the track. Papers accepted for the track will be published in the FLAIRS 2002 Conference Proceedings. The best papers will be invited for modification, extension and submission to a special issue in an international AI journal. Important dates ---------------- Paper Submission Deadline : November 15, 2001 Notification of Acceptance-Rejection : January 10, 2002 Camera Ready Copy Due : March 4, 2002 Journal Invitation : February 10, 2002 Journal Paper Due : May 10, 2002 Conference Dates : May 16-18, 2002 Program Committee ------------------ David W. Aha, Navy Center for Applied Research in AI, Washington, USA Ralph Bergmann, University of Kaiserslautern, Germany Max Bramer, University of Portsmouth, UK Colette Faucher (Chair), University of Aix-Marseille III, France Paolo Frasconi, University of Florence, Italy Robert L. Goldstone, Indiana University, USA James Hampton, City University, London, UK David Leake, Indiana University, USA Bradley C. Love, University of Texas, USA Paul Mc Kevitt, University of Ulster, Northern Ireland Ryszard S. Michalski, George Mason University, USA Philip Resnik, University of Maryland, USA Lance J. Rips, Northwestern University, USA Steven A. Sloman, Brown University, USA Paper Submission Information ----------------------------- Authors must submit an electronic copy of their complete manuscript of no more than 5 pages. All submissions must be original work. The review will be blind. Author names and affiliations are to appear ONLY on a separate cover page. The presenter (if different) from the first author must be specified on that cover page. All appropriate contact information must be mentioned for each author (e-mail, phone, fax, etc.). Papers must be written using MS Word, RTF or PDF formats according to AAAI's standard format for authors. All submissions must be sent in electronic form to : colette.faucher at iuspim.u-3mrs.fr and colette.faucher at wanadoo.fr For any problem or question, please contact the chair track, Colette Faucher, at : colette.faucher at iuspim.u-3mrs.fr or colette.faucher at wanadoo.fr. Track Website -------------- http://perso.wanadoo.fr/colette.faucher/categorization.html FLAIRS 2002 Website -------------------- http://altair.coginst.uwf.edu/~jkolen/Flairs2002/intro.php3 From wolfskil at MIT.EDU Fri Aug 17 13:17:26 2001 From: wolfskil at MIT.EDU (Jud Wolfskill) Date: Fri, 17 Aug 2001 13:17:26 -0400 Subject: book announcement--Leen Message-ID: <5.0.2.1.2.20010817115831.00ae3e28@hesiod> Hello, I thought readers of the Connectionists List might be interested in this book. For more information please visit http://mitpress.mit.edu/catalog/item/default.asp?sid=59E8DBE7-4980-48C7-A87F-F0917571FB1E&ttype=2&tid=8662 Best, Jud Advances in Neural Information Processing Systems 13 edited by Todd K. Leen, Thomas G. Dietterich, and Volker Tresp The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2000 conference. Todd K. Leen is Professor of Computer Science and Engineering, and of Electrical and Computer Engineering, at Oregon Graduate Institute of Science and Technology. Thomas G. Dietterich is Professor of Computer Science at Oregon State University. Volker Tresp heads a research group at Siemens Corporate Technology in Munich. 7 x 10, 1100 pp., cloth ISBN 0-262-12241-3 Neural Information Processing series A Bradford Book Jud Wolfskill Associate Publicist MIT Press 5 Cambridge Center, 4th Floor Cambridge, MA 02142 617.253.2079 617.253.1709 fax wolfskil at mit.edu From cindy at cns.bu.edu Fri Aug 17 10:04:17 2001 From: cindy at cns.bu.edu (Cynthia Bradford) Date: Fri, 17 Aug 2001 10:04:17 -0400 Subject: Neural Networks 14(6/7): 2001 Special Issue Message-ID: <200108171404.KAA06299@retina.bu.edu> NEURAL NETWORKS 14(6/7) Contents - Volume 14, Numbers 6/7 - 2001 2001 Special Issue "Spiking Neurons in Neuroscience and Technology" Stephen Grossberg, Wolfgang Maass, and Henry Markram, co-editors ------------------------------------------------------------------ Neural assemblies: Technical issues, analysis, and modeling George L. Gerstein and Lyle L. Kirkland Coding properties of spiking neurons: Reverse and cross-correlations Wulfram Gerstner ON-OFF retinal ganglion cells temporally encode OFF/ON sequence Hiroyuki Uchiyama, Koichi Goto, and Hiroyuki Matsunobu Building blocks for electronic spiking neural networks Andre van Schaik Orientation-selective aVLSI spiking neurons Shih-Chii Liu, Jorg Kramer, Giacomo Indiveri, Tobias Delbruck, Thomas Burg, and Rodney Douglas Space-rate coding in an adaptive silicon neuron Kai Hynna and Kwabena Boahen Propagation of cortical synfire activity: Survival probability in single trials and stability in the mean Marc-Oliver Gewaltig, Markus Diesmann, and Ad Aertsen Fokker-Planck approach to the pulse packet propagation in synfire chain H. Cateau and T. Fukai Connection topology dependence of synchronization of neural assemblies on class 1 and 2 excitability Luis F. Lago-Fernandez, Fernando J. Corbacho, and Ramon Huerta Deterministic dynamics emerging from a cortical functional architecture Ralph M. Siegel and Heather L. Read Spike-based strategies for rapid processing Simon Thorpe, Arnaud Delorme, and Rufin van Rullen Zero-lag synchronous dynamics in triplets of interconnected cortical areas D. Chawla, K.J. Friston, and E.D. Lumer Neural timing nets P.A. Cariani Spike-based VLSI modeling of the ILD system in the echolocating bat Timothy Horiuchi and Kai Hynna Pattern separation and synchronization in spiking associative memories and visual areas Andreas Knoblauch and Gunther Palm Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons David H. Goldberg, Gert Cauwenberghs, and Andreas G. Andreou Face identification using one spike per neuron: Resistance to image degradations A. Delorme and S.J. Thorpe Temporal receptive fields, spikes, and Hebbian delay selection Christian Leibold and J. Leo van Hemmen Distributed synchrony in a cell assembly of spiking neurons Nir Levy, David Horn, Isaac Meilijson, and Eytan Ruppin Associative memory in networks of spiking neurons Friedrich T. Sommer and Thomas Wennekers Trajectory estimation from place cell data Nanayaa Twum-Danso and Roger Brockett A pulsed neural network model of bursting in the basal ganglia Mark D. Humphries and Kevin N. Gurney Regularization mechanisms of spiking-bursting neurons P. Varona, J.J. Torres, R. Huerta, H.D.I. Abarbanel, and M.I. Rabinovich Optimal firing rate estimation Michael G. Paulin and Larry F. Hoffman Resonate-and-fire neurons Eugene M. Izhikevich Coherence resonance and discharge time reliability in neurons and neuronal models K. Pakdaman, Seiji Tanabe, and Tetsuya Shimokawa Adaptation in single spiking neurons based on a noise shaping neural coding hypothesis Jonghan Shin The double queue method: A numerical method for integrate-and-fire neuron networks Geehyuk Lee and Nabil H. Farhat A spiking neural network architecture for nonlinear function approximation Nicolangelo Iannella and Andrew D. Back From kenm at uwo.ca Sun Aug 19 16:02:44 2001 From: kenm at uwo.ca (Ken McRae) Date: Sun, 19 Aug 2001 16:02:44 -0400 Subject: Postdoctoral Postion Message-ID: Postdoctoral Fellowship in Psycholinguistics & Computational Modeling I have funding for a two-year Postdoctoral Fellowship in my Cognitive Science laboratory at the University of Western Ontario in London, Ontario, Canada. The stipend is $35,000 per year plus $2,500 per year for conference travel. There are no citizenship restrictions. Our research focuses on the interrelated issues of noun meaning, verb meaning, and sentence processing. Our research integrates theories and methodologies from a number of areas, including: word recognition, semantic memory, concepts and categorization, sentence processing, connectionist modeling, and cognitive neuropsychology. Central to our research program is connectionist modeling of the computation of noun and verb meaning, as well as competition-integration modeling of on-line sentence reading time. Thus, a postdoctoral fellow in my lab will have the opportunity to participate in projects in a number of areas of Cognitive Science. Our department has a number of Cognition faculty, all of whom conduct research related to language processing. Thus, our faculty and graduate students provide a rich research environment. I am also involved in a number of collaborations with researchers from other universities. My lab is well-equipped for both human experimentation and computational modeling. UWO also has a 4T magnet that is used for research only. London is a pleasant city of approximately 350,000, and is located 2 hours drive from either Toronto or Detroit. Note that a reasonable one-bedroom apartment in London costs approximately $500 per month. For further information about our lab, and Cognition at UWO, see: http://www.sscl.uwo.ca/psychology/cognitive/faculty.html If you are interested in this position, please send a cv, a statement of research interests, and 3 letters of reference to me at the address below. Sending all information electronically is preferable. The start-date for this position is flexible. If you would like more information about this position, please contact me directly. *********************************************************** Ken McRae Associate Professor Department of Psychology & Neuroscience Program Social Science Centre University of Western Ontario London, Ontario CANADA N6A 5C2 email: mcrae at uwo.ca http://www.sscl.uwo.ca/psychology/cognitive/mcrae/mcrae.html phone: (519) 661-2111 ext. 84688 fax: (519) 661-3961 *********************************************************** From cohn+jmlr at cs.cmu.edu Mon Aug 20 14:01:50 2001 From: cohn+jmlr at cs.cmu.edu (JMLR) Date: Mon, 20 Aug 2001 14:01:50 -0400 Subject: New paper in the Journal of Machine Learning Research: Bayes Point Machines Message-ID: The Journal of Machine Learning Research (www.jmlr.org) is pleased to announce the availability of a new paper in electronic form. ---------------------------------------- Bayes Point Machines Ralf Herbrich, Thore Graepel and Colin Campbell. Journal of Machine Learning Research 1 (August 2001), pp. 245-279. Abstract Kernel-classifiers comprise a powerful class of non-linear decision functions for binary classification. The support vector machine is an example of a learning algorithm for kernel classifiers that singles out the consistent classifier with the largest margin, i.e. minimal real-valued output on the training sample, within the set of consistent hypotheses, the so-called version space. We suggest the Bayes point machine as a well-founded improvement which approximates the Bayes-optimal decision by the centre of mass of version space. We present two algorithms to stochastically approximate the centre of mass of version space: a billiard sampling algorithm and a sampling algorithm based on the well known perceptron algorithm. It is shown how both algorithms can be extended to allow for soft-boundaries in order to admit training errors. Experimentally, we find that - for the zero training error case - Bayes point machines consistently outperform support vector machines on both surrogate data and real-world benchmark data sets. In the soft-boundary/soft-margin case, the improvement over support vector machines is shown to be reduced. Finally, we demonstrate that the real-valued output of single Bayes points on novel test points is a valid confidence measure and leads to a steady decrease in generalisation error when used as a rejection criterion. This paper and earlier papers in Volume 1 are available electronically at http://www.jmlr.org in PostScript, PDF and HTML formats; a bound, hardcopy edition of Volume 1 will be available later this year. -David Cohn, Managing Editor, Journal of Machine Learning Research ------- This message has been sent to the mailing list "jmlr-announce at ai.mit.edu", which is maintained automatically by majordomo. To subscribe to the list, send mail to listserv at ai.mit.edu with the line "subscribe jmlr-announce" in the body; to unsubscribe send email to listserv at ai.mit.edu with the line "unsubscribe jmlr-announce" in the body. From jf218 at hermes.cam.ac.uk Mon Aug 20 17:15:19 2001 From: jf218 at hermes.cam.ac.uk (Dr J. Feng) Date: Mon, 20 Aug 2001 22:15:19 +0100 (BST) Subject: five years post at cambridge In-Reply-To: <200108171404.KAA06299@retina.bu.edu> Message-ID: The Babraham Institute, Cambridge Computational Neuroscientist/Electrophysiologist (Ref. KK/CNE) Applications are invited for a postdoctoral scientist to join a group of systems neuroscientists within the Laboratory of Cognitive and Developmental Neuroscience investigating how the brain encodes visual and olfactory cues associated with recognition or both social and non-social objects using novel multi-array electrophysiological recording techniques in both rodent and sheep models. This post is available initially for 5 years. It would either suit an individual with primary expertise in computational analysis and modelling of sensory system functioning or an in vivo electrophysiologist with good expertise in computational analysis of complex single-unit data. In both cases there would be significant involvement in carrying out multi-array electrophysiological recording experiments and subsequent data analysis and representation. The individual would also be expected to work closely with electrophysiologists both within the group and the USA and to co-ordinate with other UK-based Computational Neuroscientists involved with the projects. The group already has excellent computational facilities to deal with the large amounts data associated with multi-array recording experiments Informal enquiries on these Neuroscience vacancies should be directed to Dr. Keith Kendrick, Head of Neurobiology Programme: tel: 44(0) 1223 496385, fax. 44(0)1223 496028, e-mail keith.kendrick at bbsrc.ac.uk Starting salary in the range ?19,500 - ?23,000 per annum. Benefits include a non-contributory pension scheme, 25 days leave and 10? public holidays a year. On site Refectory, Nursery and Sports & Social Club as well as free car parking. Further details and an application form available from the Personnel Office, The Babraham Institute, Babraham, Cambridge CB2 4AT. Tel. 01223 496000, e-mail babraham.personnel at bbsrc.ac.uk. The closing date for these positions is 28th September 2001. AN EQUAL OPPORTUNITIES EMPLOYER An Institute supported by the Biotechnology and Biological Sciences Research Council Jianfeng Feng The Babraham Institute Cambridge CB2 4AT UK http://www.cosg.susx.ac.uk/users/jianfeng http://www.cus.cam.ac.uk/~jf218 From wolfskil at MIT.EDU Mon Aug 20 10:25:54 2001 From: wolfskil at MIT.EDU (Jud Wolfskill) Date: Mon, 20 Aug 2001 10:25:54 -0400 Subject: book announcement--O'Reilly Message-ID: <5.0.2.1.2.20010820102443.00a82000@hesiod> I thought readers of the Connectionists List might be interested in this book. For more information please visit http://mitpress.mit.edu/catalog/item/default.asp?sid=16CDFF8A-3F4A-4FB5-B713-D8725D0A6969&ttype=2&tid=3345 Best, Jud Computational Explorations in Cognitive Neuroscience Understanding the Mind by Simulating the Brain Randall C. O'Reilly and Yuko Munakata foreword by James L. McClelland The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprising networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons, and the neural networks incorporate anatomical and physiological properties of the neocortex. Thus the text provides the student with knowledge of the basic biology of the brain as well as the computational skills needed to simulate large-scale cognitive phenomena. The text consists of two parts. The first part covers basic neural computation mechanisms: individual neurons, neural networks, and learning mechanisms. The second part covers large-scale brain area organization and cognitive phenomena: perception and attention, memory, language, and higher-level cognition. The second part is relatively self-contained and can be used separately for mechanistically oriented cognitive neuroscience courses. Integrated throughout the text are more than forty different simulation models, many of them full-scale research-grade models, with friendly interfaces and accompanying exercises. The simulation software (PDP++, available for all major platforms) and simulations can be downloaded free of charge from the Web. Exercise solutions are available, and the text includes full information on the software. Randall C. O'Reilly is Assistant Professor in the Department of Psychology and at the Institute for Cognitive Science at the University of Colorado, Boulder. Yuko Munakata is Assistant Professor in Developmental Cognitive Neuroscience at the University of Denver. 8 x 9, 512 pp., 213 illus., paper ISBN 0-262-65054-1 A Bradford Book Jud Wolfskill Associate Publicist MIT Press 5 Cambridge Center, 4th Floor Cambridge, MA 02142 617.253.2079 617.253.1709 fax wolfskil at mit.edu From ps629 at columbia.edu Tue Aug 21 15:54:59 2001 From: ps629 at columbia.edu (Paul Sajda) Date: Tue, 21 Aug 2001 15:54:59 -0400 Subject: Postdoctoral Position in Computational Neural Modeling Message-ID: <3B82BC93.34337935@columbia.edu> Postdoctoral Position in Computational Neural Modeling--a two year position is available immediately for conducting research in modeling of neural mechanisms for visual scene analysis, with particular applications to spatio-temporal and hyperspectral imagery. A mathematical and computational background is desired, particularly in probabilistic modeling and optimization. This position will be part of a multi-university research team (UPenn, Columbia and MIT) investigating biomimetic methods for analysis of literal and non-literal imagery through a combination of experimental physiology, neuromorphic design and simulation, computational modeling and visual psycophysics. Applicants should send a CV, three representative papers and the names of three references to Prof. Paul Sajda, Department of Biomedical Engineering, Columbia University, 530 W 120th Street, NY, NY 10027. Or email to ps629 at columbia.edu. -- Paul Sajda, Ph.D. Associate Professor Department of Biomedical Engineering 530 W 120th Street Columbia University New York, NY 10027 tel: (212) 854-5279 fax: (212) 854-8725 email: ps629 at columbia.edu http://www.columbia.edu/~ps629 From wolfskil at MIT.EDU Wed Aug 22 14:17:30 2001 From: wolfskil at MIT.EDU (Jud Wolfskill) Date: Wed, 22 Aug 2001 14:17:30 -0400 Subject: book announcement--Opper Message-ID: <5.0.2.1.2.20010822140915.00b083c0@hesiod> I thought readers of the Connectionists List might be interested in this book. For more information please visit http://mitpress.mit.edu/catalog/item/default.asp?sid=5CEC3656-296C-4C48-B6E3-6BDFAC7EBADD&ttype=2&tid=3847 Best, Jud Advanced Mean Field Methods Theory and Practice edited by Manfred Opper and David Saad A major problem in modern probabilistic modeling is the huge computational complexity involved in typical calculations with multivariate probability distributions when the number of random variables is large. Because exact computations are infeasible in such cases and Monte Carlo sampling techniques may reach their limits, there is a need for methods that allow for efficient approximate computations. One of the simplest approximations is based on the mean field method, which has a long history in statistical physics. The method is widely used, particularly in the growing field of graphical models. Researchers from disciplines such as statistical physics, computer science, and mathematical statistics are studying ways to improve this and related methods and are exploring novel application areas. Leading approaches include the variational approach, which goes beyond factorizable distributions to achieve systematic improvements; the TAP (Thouless-Anderson-Palmer) approach, which incorporates correlations by including effective reaction terms in the mean field theory; and the more general methods of graphical models. Bringing together ideas and techniques from these diverse disciplines, this book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling. Manfred Opper is a Reader and David Saad is Professor, the Neural Computing Research Group, School of Engineering and Applied Science, Aston University, UK. 7 x 10, 300 pp. cloth ISBN 0-262-15054-9 Neural Information Processing series Jud Wolfskill Associate Publicist MIT Press 5 Cambridge Center, 4th Floor Cambridge, MA 02142 617.253.2079 617.253.1709 fax wolfskil at mit.edu From abrowne at lgu.ac.uk Thu Aug 23 07:50:50 2001 From: abrowne at lgu.ac.uk (Tony Browne) Date: Thu, 23 Aug 2001 12:50:50 +0100 (GMT Daylight Time) Subject: Connectionist Inference Preprint Message-ID: Apologies if you receive this posting more than once. A preprint is available for download, of the paper 'Connectionist Inference Models' by Antony Browne and Ron Sun (to appear in `Neural Networks'). 62 Pages, 155 References. Abstract: The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling. Keywords: Symbolic inference, resolution, variable binding, localist representations, distributed representations. Download Instructions: Go to http://www.lgu.ac.uk/~abrowne/abrowne.htm and scroll down to the section 'Downloadable Technical Reports and Preprints'. Click on the file to download (in zipped Postscript [190K] or Zipped PDF [228K] format). Comments Welcome If you have problems downloading, please e-mail me. Tony Browne ======================================================= Dr. Antony Browne abrowne at lgu.ac.uk http://www.lgu.ac.uk/~abrowne/abrowne.htm Reader in Intelligent Systems School of Computing, Information Systems & Mathematics London Guildhall University 100 Minories London EC3 1JY, UK Tel: (+44) 0207 320 1307 Fax: (+44) 0207 320 1717 ======================================================= From stefan.wermter at sunderland.ac.uk Thu Aug 23 13:02:13 2001 From: stefan.wermter at sunderland.ac.uk (Stefan.Wermter) Date: Thu, 23 Aug 2001 18:02:13 +0100 Subject: EmerNet book: Emergent Neural Computational Architectures Message-ID: <3B853714.6E58E4CA@sunderland.ac.uk> Emergent Neural Computational Architectures based on Neuroscience Stefan Wermter, Jim Austin, David Willshaw 2001, Springer, Heidelberg, 577p For more detailed information, table of contents, abstracts and chapters see: http://www.his.sunderland.ac.uk/emernet/newbook.html Summary: This book is the result of a series of International Workshops organised by the EmerNet project on Emergent Neural Computational Architectures based on Neuroscience sponsored by the Engineering and Physical Sciences Research Council (EPSRC). The overall aim of the book is to present a broad spectrum of current research into biologically inspired computational systems and hence encourage the emergence of new computational approaches based on neuroscience. It is generally understood that the present approaches for computing do not have the performance, flexibility and reliability of biological information processing systems. Although there is a massive body of knowledge regarding how processing occurs in the brain and central nervous system this has had little impact on mainstream computing so far. The process of developing biologically inspired computerised systems involves the examination of the functionality and architecture of the brain with an emphasis on the information processing activities. Biologically inspired computerised systems address neural computation from the position of both neuroscience, and computing by using experimental evidence to create general neuroscience-inspired systems. The book focuses on the main research areas of modular organisation and robustness, timing and synchronisation, and learning and memory storage. The issues considered as part of these include: How can the modularity in the brain be used to produce large scale computational architectures? How does the human memory manage to continue to operate despite failure of its components? How does the brain synchronise its processing? How does the brain compute with relatively slow computing elements but still achieve rapid and real-time performance? How can we build computational models of these processes and architectures? How can we design incremental learning algorithms and dynamic memory architectures? How can the natural information processing systems be exploited for artificial computational methods? Emergent Neural Computational Architectures based on Neuroscience can be ordered from Springer-Verlag using the booking form and accessed on-line using the appropriate login and password from Springer. http://www.his.sunderland.ac.uk/emernet/newbook.html http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-42363-X -------------------------------------- *************************************** Professor Stefan Wermter Chair for Intelligent Systems University of Sunderland Centre of Informatics, SCET St Peters Way Sunderland SR6 0DD United Kingdom phone: +44 191 515 3279 fax: +44 191 515 3553 email: stefan.wermter at sunderland.ac.uk http://www.his.sunderland.ac.uk/~cs0stw/ http://www.his.sunderland.ac.uk/ **************************************** From rid at ecs.soton.ac.uk Fri Aug 24 05:56:32 2001 From: rid at ecs.soton.ac.uk (Bob Damper) Date: Fri, 24 Aug 2001 10:56:32 +0100 (BST) Subject: Source of a famous quotation ... Message-ID: Dear connectionists, does anyone know the exact source of the famous quotation: ``neural networks are the second best way of solving every problem'' ? I'd be eternally grateful for an answer. Bob. *************************************************************** * R I Damper PhD * * Reader and Head: * * Image, Speech and Intelligent Systems (ISIS) * * Research Group * * Building 1 * * Department of Electronics and Computer Science * * University of Southampton * * Southampton SO17 1BJ * * England * * * * Tel: +44 (0) 23 8059 4577 (direct) * * FAX: +44 (0) 23 8059 4498 * * Email: rid at ecs.soton.ac.uk * * WWW: http://www.ecs.soton.ac.uk/~rid * * * *************************************************************** From gabr-ci0 at wpmail.paisley.ac.uk Fri Aug 24 12:40:10 2001 From: gabr-ci0 at wpmail.paisley.ac.uk (Bogdan Gabrys) Date: Fri, 24 Aug 2001 17:40:10 +0100 Subject: PhD studentship available Message-ID: PhD Studentship Applied Computational Intelligence Research Unit (ACIRU) School of Information and Communication Technologies, University of Paisley, Scotland, UK Applications are invited for a 3 year PhD research studentship which can start from October 2001 and is jointly funded by the University of Paisley (http://www.cis.paisley.ac.uk) and the Lufthansa Systems Berlin GmbH (http://www.lsb.de). The proposed research project will investigate and develop various approaches for combining predictions (forecasts). There is a large potential market for applications offering accurate and reliable predictions ranging from stock market exchange to estimating the demand for sales of goods and services. One such example, which will be looked at in more detail in this project, is an accurate estimation of the demand for various types of airplane tickets. Combination, aggregation and fusion of information are major problems for all kinds of knowledge-based systems, from image processing to decision making, from pattern recognition to automatic learning. Various machine learning and hybrid intelligent techniques will be used for processing and modelling of imperfect data and information utilizing the methodologies like probability, fuzzy, evidence and possibility theories. The student will be joining an enthusiastic and vibrant research group and will be primarily based in the ACIRU in Paisley (near Glasgow), Scotland but two extended visits to the Lufthansa Systems Berlin site in Berlin, Germany are planned in the second and third year of the project. The studentship carries a remuneration of ?7500 tax-free (increased to ?8k and ?9k in the second and third year respectively) and payment of tuition fees paid at Home/EU rate. The stipend may be augmented by a limited amount of teaching. Applicants should have a strong mathematical background and hold a first or upper second class honours degree or equivalent in mathematics, physics, engineering, statistics, computer science or a similar discipline. Additionally the candidate should have strong programming experience using any or combination of C,C++,Matlab or Java. Knowledge of ORACLE will be an advantage. For further details please contact Dr. Bogdan Gabrys, e-mail: gabr-ci0 at paisley.ac.uk. Interested candidates should send a detailed CV and a letter of application with the names and addresses of two referees to: Dr. Bogdan Gabrys, School of Information and Communication Technologies, Div. of Computing and Information Systems, University of Paisley, Paisley PA1 2BE, Scotland, UK. The applications can be also sent by e-mail. ****************************************************** Dr Bogdan Gabrys Applied Computational Intelligence Research Unit Division of Computing and Information Systems University of Paisley High Street, Paisley PA1 2BE Scotland, United Kingdom Tel: +44 (0) 141 848 3752 Fax: +44 (0) 141 848 3542 E-mail: gabr-ci0 at paisley.ac.uk ****************************************************** Legal disclaimer -------------------------- The information transmitted is the property of the University of Paisley and is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. Statements and opinions expressed in this e-mail may not represent those of the company. Any review, retransmission, dissemination and other use of, or taking of any action in reliance upon, this information by persons or entities other than the intended recipient is prohibited. If you received this in error, please contact the sender immediately and delete the material from any computer. -------------------------- From cindy at cns.bu.edu Fri Aug 24 16:34:07 2001 From: cindy at cns.bu.edu (Cynthia Bradford) Date: Fri, 24 Aug 2001 16:34:07 -0400 Subject: Call for Papers: 6th ICCNS Message-ID: <200108242034.QAA17997@retina.bu.edu> Apologies if you receive this more than once. ***** CALL FOR PAPERS ***** SIXTH INTERNATIONAL CONFERENCE ON COGNITIVE AND NEURAL SYSTEMS Tutorials: May 29, 2002 Meeting: May 30 - June 1, 2002 Boston University 677 Beacon Street Boston, Massachusetts 02215 http://www.cns.bu.edu/meetings/ Sponsored by Boston University's Center for Adaptive Systems and Department of Cognitive and Neural Systems with financial support from the National Science Foundation and the Office of Naval Research This interdisciplinary conference has drawn about 300 people from around the world each time that it has been offered. Last year's conference was attended by scientists from 31 countries. The conference is structured to facilitate intense communication between its participants, both in the formal sessions and during its other activities. As during previous years, the conference will focus on solutions to the fundamental questions: How Does the Brain Control Behavior? How Can Technology Emulate Biological Intelligence? The conference will include invited tutorials and lectures, and contributed lectures and posters by experts on the biology and technology of how the brain and other intelligent systems adapt to a changing world. The conference is aimed at researchers and students of computational neuroscience, connectionist cognitive science, artificial neural networks, neuromorphic engineering, and artificial intelligence. A single oral or poster session enables all presented work to be highly visible. Abstract submissions encourage submissions of the latest results. Costs are kept at a minimum without compromising the quality of meeting handouts and social events. CALL FOR ABSTRACTS Session Topics: * vision * spatial mapping and navigation * object recognition * neural circuit models * image understanding * neural system models * audition * mathematics of neural systems * speech and language * robotics * unsupervised learning * hybrid systems (fuzzy, evolutionary, digital) * supervised learning * neuromorphic VLSI * reinforcement and emotion * industrial applications * sensory-motor control * cognition, planning, and attention * other Contributed abstracts must be received, in English, by January 31, 2002. Notification of acceptance will be provided by email by February 28, 2002. A meeting registration fee must accompany each Abstract. See Registration Information below for details. The fee will be returned if the Abstract is not accepted for presentation and publication in the meeting proceedings. Registration fees of accepted Abstracts will be returned on request only until April 19, 2002. Each Abstract should fit on one 8.5" x 11" white page with 1" margins on all sides, single-column format, single-spaced, Times Roman or similar font of 10 points or larger, printed on one side of the page only. Fax submissions will not be accepted. Abstract title, author name(s), affiliation(s), mailing, and email address(es) should begin each Abstract. An accompanying cover letter should include: Full title of Abstract; corresponding author and presenting author name, address, telephone, fax, and email address; requested preference for oral or poster presentation; and a first and second choice from the topics above, including whether it is biological (B) or technological (T) work. Example: first choice: vision (T); second choice: neural system models (B). (Talks will be 15 minutes long. Posters will be up for a full day. Overhead, slide, VCR, and LCD projector facilities will be available for talks.) Abstracts which do not meet these requirements or which are submitted with insufficient funds will be returned. Accepted Abstracts will be printed in the conference proceedings volume. No longer paper will be required. The original and 3 copies of each Abstract should be sent to: Cynthia Bradford, Boston University, Department of Cognitive and Neural Systems, 677 Beacon Street, Boston, MA 02215. REGISTRATION INFORMATION: Early registration is recommended. To register, please fill out the registration form below. Student registrations must be accompanied by a letter of verification from a department chairperson or faculty/research advisor. If accompanied by an Abstract or if paying by check, mail to the address above. If paying by credit card, mail as above, or fax to (617) 353-7755, or email to cindy at cns.bu.edu. The registration fee will help to pay for a reception, 6 coffee breaks, and the meeting proceedings. STUDENT FELLOWSHIPS: Fellowships for PhD candidates and postdoctoral fellows are available to help cover meeting travel and living costs. The deadline to apply for fellowship support is January 31, 2002. Applicants will be notified by email by February 28, 2002. Each application should include the applicant's CV, including name; mailing address; email address; current student status; faculty or PhD research advisor's name, address, and email address; relevant courses and other educational data; and a list of research articles. A letter from the listed faculty or PhD advisor on official institutional stationery should accompany the application and summarize how the candidate may benefit from the meeting. Fellowship applicants who also submit an Abstract need to include the registration fee with their Abstract submission. Those who are awarded fellowships are required to register for and attend both the conference and the day of tutorials. Fellowship checks will be distributed after the meeting. REGISTRATION FORM Sixth International Conference on Cognitive and Neural Systems Department of Cognitive and Neural Systems Boston University 677 Beacon Street Boston, Massachusetts 02215 Tutorials: May 29, 2002 Meeting: May 30 - June 1, 2002 FAX: (617) 353-7755 http://www.cns.bu.edu/meetings/ (Please Type or Print) Mr/Ms/Dr/Prof: _____________________________________________________ Name: ______________________________________________________________ Affiliation: _______________________________________________________ Address: ___________________________________________________________ City, State, Postal Code: __________________________________________ Phone and Fax: _____________________________________________________ Email: _____________________________________________________________ The conference registration fee includes the meeting program, reception, two coffee breaks each day, and meeting proceedings. The tutorial registration fee includes tutorial notes and two coffee breaks. CHECK ONE: ( ) $85 Conference plus Tutorial (Regular) ( ) $55 Conference plus Tutorial (Student) ( ) $60 Conference Only (Regular) ( ) $40 Conference Only (Student) ( ) $25 Tutorial Only (Regular) ( ) $15 Tutorial Only (Student) METHOD OF PAYMENT (please fax or mail): [ ] Enclosed is a check made payable to "Boston University". Checks must be made payable in US dollars and issued by a US correspondent bank. Each registrant is responsible for any and all bank charges. [ ] I wish to pay my fees by credit card (MasterCard, Visa, or Discover Card only). Name as it appears on the card: _____________________________________ Type of card: _______________________________________________________ Account number: _____________________________________________________ Expiration date: ____________________________________________________ Signature: __________________________________________________________ From jzhu at stanford.edu Fri Aug 24 12:35:24 2001 From: jzhu at stanford.edu (Ji Zhu) Date: Fri, 24 Aug 2001 09:35:24 -0700 (PDT) Subject: No subject Message-ID: Dear all, This is a repost of our paper "Kernel Logistic Regression and the Import Vector Machine". We want to apologize that we missed several important references in our previous draft. The revised version is available at http://www.stanford.edu/~jzhu/research/nips01.ps Thank you! Best regards, -Ji Zhu From skremer at q.cis.uoguelph.ca Mon Aug 27 16:29:04 2001 From: skremer at q.cis.uoguelph.ca (Stefan C. Kremer) Date: Mon, 27 Aug 2001 16:29:04 -0400 (EDT) Subject: Announce: New Unlabeled Data Competition and Workshop Message-ID: Apologies if you receive multiple copies of this mailing. ANNOUNCEMENT: The Second Annual NIPS Unlabeled Data Competition and Workshop It's time to put-up or shut-up! Synopsis: We are please to announce the NIPS*2001 Unlabeled Data Competition and Workshop, to be held in Whistler, British Columbia, Canada, Dec 7 or 8, 2001. This competition is a challenge to the machine learning community to develop and demonstrate methods to use unlabeled data to improve supervised learning. We have created a web-site where participants can download and submit problem sets and compete head to head with other contestants in a series of challenging unlabeled-data, supervised-learning problems. Recently, there has been much interest in applying techniques that incorporate knowledge from unlabeled data into systems performing supervised learning. The potential advantages of such techniques are obvious in domains where labeled data is expensive and unlabeled data is cheap. Many such techniques have been proposed, but only recently has any effort been made to compare the effectiveness of different approaches on real world problems. Our contest presents a challenge to the proponents of methods to incorporate unlabeled data into supervised learning. Can you really use unlabeled data to help train a supervised classification (or regression) system? Do recent (and not so recent) theories stand up to the data test? On the contest web-site you can find challenge problems where you can try out your methods head-to-head against anyone brave enough to face you. Then, at the end of the contest we will release the results and find out who really knows something about using unlabeled data, and if unlabeled data are really useful or we are all just wasting our time. So ask yourself, are you (and your theory) up to the challenge?? Feeling lucky??? For more details on the competition or the workshop and to sign up for the Unlabeled Data Mailing List, please visit our web-page at "http://q.cis.uoguelph.ca/~skremer/NIPS2001/". Stefan -- -- Dr. Stefan C. Kremer, Assistant Prof., Dept. of Computing and Information Science University of Guelph, Guelph, Ontario N1G 2W1 WWW: http://hebb.cis.uoguelph.ca/~skremer Tel: (519)824-4120 Ext.8913 Fax: (519)837-0323 E-mail: skremer at snowhite.cis.uoguelph.ca From bbs at bbsonline.org Tue Aug 28 16:55:50 2001 From: bbs at bbsonline.org (Stevan Harnad - Behavioral & Brain Sciences (Editor)) Date: Tue, 28 Aug 2001 16:55:50 -0400 Subject: BBS Call for Commentators--Preston & De Waal: Empathy: Its ultimate and proximate bases Message-ID: Dear Dr. Connectionists List User, Below is the abstract of a forthcoming BBS target article Empathy: Its ultimate and proximate bases by Stephanie D. Preston & Frans B. M. de Waal http://www.bbsonline.org/Preprints/Preston/ or http://www.bbsonline.org/Preprints/Preston/Preston.pdf This article has been accepted for publication in Behavioral and Brain Sciences (BBS), an international, interdisciplinary journal providing Open Peer Commentary on important and controversial current research in the biobehavioral and cognitive sciences. Commentators must be BBS Associates or nominated by a BBS Associate. To be considered as a commentator for this article, to suggest other appropriate commentators, or for information about how to become a BBS Associate, please reply by EMAIL within three (3) weeks to: calls at bbsonline.org The Calls are sent to 10,000 BBS Associates, so there is no expectation (indeed, it would be calamitous) that each recipient should comment on every occasion! Hence there is no need to reply except if you wish to comment, or to nominate someone to comment. If you are not a BBS Associate, please approach a current BBS Associate (there are currently over 10,000 worldwide) who is familiar with your work to nominate you. All past BBS authors, referees and commentators are eligible to become BBS Associates. A full electronic list of current BBS Associates is available at this location to help you select a name: http://www.bbsonline.org/Instructions/assoclist.html If no current BBS Associate knows your work, please send us your Curriculum Vitae and BBS will circulate it to appropriate Associates to ask whether they would be prepared to nominate you. (In the meantime, your name, address and email address will be entered into our database as an unaffiliated investigator.) To help us put together a balanced list of commentators, please give some indication of the aspects of the topic on which you would bring your areas of expertise to bear if you were selected as a commentator. To help you decide whether you would be an appropriate commentator for this article, an electronic draft is retrievable from the online BBSPrints Archive, at the URL that follows the abstract below. _____________________________________________________________ Empathy: Its ultimate and proximate bases Stephanie D. Preston Department of Psychology 3210 Tolman Hall #1650 University of California at Berkeley Berkeley, CA 94720-1650 USA spreston at socrates.berkeley.edu http://socrates.berkeley.edu/~spreston Frans B. M. de Waal Living Links, Yerkes Primate Center and Psychology Department, Emory University, Atlanta, GA 30322 USA dewaal at rmy.emory.edu http://www.emory.edu/LIVING_LINKS/ KEYWORDS: altruism; cognitive empathy; comparative; emotion; emotional contagion; empathy; evolution; human; perception-action; perspective taking; ABSTRACT: There is disagreement in the literature about the exact nature of the phenomenon of empathy. There are emotional, cognitive, and conditioning views, applying in varying degrees across species. An adequate description of the ultimate and proximate mechanism can integrate these views. Proximately, the perception of an object's state activates the subject's corresponding representations, which in turn activate somatic and autonomic responses. This mechanism supports basic behaviors (e.g., alarm, social facilitation, vicariousness of emotions, mother-infant responsiveness, and the modeling of competitors and predators) that are crucial for the reproductive success of animals living in groups. The "Perception-Action Model" (PAM) together with an understanding of how representations change with experience can explain the major empirical effects in the literature (similarity, familiarity, past experience, explicit teaching and salience). It can also predict a variety of empathy disorders. The interaction between the PAM and prefrontal functioning can also explain different levels of empathy across species and age groups. This view can advance our evolutionary understanding of empathy beyond inclusive fitness and reciprocal altruism and can explain different levels of empathy across individuals, species, stages of development, and situations. http://www.bbsonline.org/Preprints/Preston/ or http://www.bbsonline.org/Preprints/Preston/Preston.pdf ___________________________________________________________ Please do not prepare a commentary yet. Just let us know, after having inspected it, what relevant expertise you feel you would bring to bear on what aspect of the article. We will then let you know whether it was possible to include your name on the final formal list of invitees. _______________________________________________________________________ *** SUPPLEMENTARY ANNOUNCEMENTS *** (1) The authors of scientific articles are not paid money for their refereed research papers; they give them away. What they want is to reach all interested researchers worldwide, so as to maximize the potential research impact of their findings. Subscription/Site-License/Pay-Per-View costs are accordingly access-barriers, and hence impact-barriers for this give-away research literature. There is now a way to free the entire refereed journal literature, for everyone, everywhere, immediately, by mounting interoperable university eprint archives, and self-archiving all refereed research papers in them. Please see: http://www.eprints.org http://www.openarchives.org/ http://www.dlib.org/dlib/december99/12harnad.html --------------------------------------------------------------------- (2) All authors in the biobehavioral and cognitive sciences are strongly encouraged to self-archive all their papers in their own institution's Eprint Archives or in CogPrints, the Eprint Archive for the biobehavioral and cognitive sciences: http://cogprints.soton.ac.uk/ It is extremely simple to self-archive and will make all of our papers available to all of us everywhere, at no cost to anyone, forever. Authors of BBS papers wishing to archive their already published BBS Target Articles should submit it to BBSPrints Archive. Information about the archiving of BBS' entire backcatalogue will be sent to you in the near future. Meantime please see: http://www.bbsonline.org/help/ and http://www.bbsonline.org/Instructions/ --------------------------------------------------------------------- (3) Call for Book Nominations for BBS Multiple Book Review In the past, Behavioral and Brain Sciences (BBS) had only been able to do 1-2 BBS multiple book treatments per year, because of our limited annual page quota. BBS's new expanded page quota will make it possible for us to increase the number of books we treat per year, so this is an excellent time for BBS Associates and biobehavioral/cognitive scientists in general to nominate books you would like to see accorded BBS multiple book review. (Authors may self-nominate, but books can only be selected on the basis of multiple nominations.) It would be very helpful if you indicated in what way a BBS Multiple Book Review of the book(s) you nominate would be useful to the field (and of course a rich list of potential reviewers would be the best evidence of its potential impact!). *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* Please note: Your email address has been added to our user database for Calls for Commentators, the reason you received this email. If you do not wish to receive further Calls, please feel free to change your mailshot status through your User Login link on the BBSPrints homepage, useing your username and password above: http://www.bbsonline.org/ For information about the mailshot, please see the help file at: http://www.bbsonline.org/help/node5.html#mailshot *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* From brody at cshl.org Tue Aug 28 18:35:49 2001 From: brody at cshl.org (Carlos Brody) Date: Tue, 28 Aug 2001 18:35:49 -0400 (EDT) Subject: Postdoctoral positions in computational neuroscience Message-ID: <15244.7365.990066.563845@sonnabend.cshl.org> -- PLEASE POST -- POSTDOCTORAL OPPORTUNITIES IN COMPUTATIONAL NEUROSCIENCE Postdoctoral positions for computational neuroscientists and psychophysicists are available in Carlos Brody's research group at Cold Spring Harbor Laboratory. (see http://www.cns.caltech.edu/~carlos/temporary/Lab). Applicants should have an interest in quantitative approaches to neuroscience, and should have, or be near completing, a Ph.D. in Neuroscience, Experimental Psychology, or in a quantitative field (e.g. Physics, Math, Engineering). Successful applicants will be expected, after appropriate guidance and/or any necessary self-education, to lead the group's research efforts in one or more of the projects listed below. For more information on each of these projects, visit the lab's web page. In addition, those who wish to develop and pursue their own, independent, self-originated, line(s) of research will be very much encouraged to do so: the lab seeks an atmosphere of vigorous discussion and creative independence. Applications from self-guided, motivated, and independent-minded scientists are particularly welcome. Applicants should send a CV, the names of three references, and a summary of research interests and experience to: Carlos Brody, 1 Bungtown Road, Freeman Building, Cold Spring Harbor, NY 11724, USA. The positions are open immediately; salaries are on the NIH pay scale. ---------- Lab interest areas (in order of descending current emphasis in the lab): 1) Psychophysics and neurocomputational modeling of working memory. 2) Encoding and representation of time. 3) Computation with spiking neurons. 4) Automated mapping of complex receptive fields. From engp9286 at nus.edu.sg Wed Aug 29 03:39:47 2001 From: engp9286 at nus.edu.sg (Duan Kaibo) Date: Wed, 29 Aug 2001 15:39:47 +0800 Subject: a technical report Message-ID: <9C4C56CDF89E0440A6BD571E76D2387FB7559B@exs23.ex.nus.edu.sg> Dear Connectionists: We have recently completed a technical report that evaluates some simple performance measures for tuning hyperparameters of Support Vector Machines. A pdf file containing this report can be downloaded from: http://guppy.mpe.nus.edu.sg/~mpessk/comparison.shtml Here are the details of the report... __________________________________________________________________ Title: Evaluation of Simple Performance Measures for Tuning SVM Hyperparameters Authors: Kaibo Duan ( engp9286 at nus.edu.sg ) S. Sathiya Keerthi ( mpessk at nus.edu.sg ) Aun Neow Poo ( mpepooan at nus.edu.sg ) Abstract: Choosing optimal hyperparameter values for support vector machines is an important step in SVM design. This is usually done by minimizing either an estimate of generalization error or some other related performance measure. In this paper, we empirically study the usefulness of several simple performance measures that are inexpensive to compute (in the sense that they do not require expensive matrix operations involving the kernel matrix). The results point out which of these measures are adequate functionals for tuning SVM hyperparameters. For SVMs with L1 soft margin formulation, none of the simple measures yields a performance uniformly as good as k-fold cross validation; Joachims' Xi-Alpha bound and Wahba et al's GACV come next and perform reasonably well. For SVMs with L2 soft margin formulation, the radius margin bound gives a very good prediction of optimal hyperparameter values. __________________________________________________________________ We are interested in knowing about the comparitive performance of the measures that we have considered, on other data sets that we haven't tried. Best regards, Kaibo From mike at stats.gla.ac.uk Wed Aug 29 10:17:12 2001 From: mike at stats.gla.ac.uk (Mike Titterington) Date: Wed, 29 Aug 2001 15:17:12 +0100 (BST) Subject: Postdoctoral post in Glasgow Message-ID: (Re-advertisement) UNIVERSITY OF GLASGOW DEPARTMENT OF STATISTICS POSTDOCTORAL RESEARCH ASSISTANT Applications are invited for a Postdoctoral Research Assistantship (IA) post in the Department of Statistics, University of Glasgow, to work with Professor D.M. Titterington for a period of up to 3 years, starting as soon as possible. The post is funded by the UK Engineering and Physical Sciences Research Council. The research topic is 'Approximate Approaches to Likelihood and Bayesian Statistical Inference in Incomplete-data problems'. Applications, supported by full curriculum vitae and the names of three referees, should be sent, to arrive no later than September 21, 2001, to Professor D. M. Titterington, Department of Statistics, University of Glasgow, Glasgow G12 8QQ, Scotland, from whom further particulars are available. Informal enquiries by electronic mail (mike at stats.gla.ac.uk) are welcomed. From juergen at idsia.ch Thu Aug 30 10:57:29 2001 From: juergen at idsia.ch (juergen@idsia.ch) Date: Thu, 30 Aug 2001 16:57:29 +0200 Subject: metalearner Message-ID: <200108301457.QAA01240@ruebe.idsia.ch> I would like to draw your attention to Sepp Hochreiter's astonishing recent result on "learning to learn." He trains gradient-based "Long Short-Term Memory" (LSTM) recurrent networks with roughly 5000 weights to _metalearn_ fast online learning algorithms for nontrivial classes of functions, such as all quadratic functions of two variables. LSTM is necessary because metalearning typically involves huge time lags between important events, and standard gradient-based recurrent nets cannot deal with these. After a month of metalearning on a PC he freezes all weights, then uses the frozen net as follows: He selects some new function f, and feeds a sequence of random training exemplars of the form ...data/target/data/target/data... into the input units, one sequence element at a time. After about 30 exemplars the frozen recurrent net correctly predicts target inputs before it sees them. No weight changes! How is this possible? After metalearning the frozen net implements a sequential learning algorithm which apparently computes something like error signals from data inputs and target inputs and translates them into changes of internal estimates of f. Parameters of f, errors, temporary variables, counters, computations of f and of parameter updates are all somehow represented in form of circulating activations. Remarkably, the new - and quite opaque - online learning algorithm running on the frozen network is much faster than standard backprop with optimal learning rate. This indicates that one can use gradient descent to metalearn learning algorithms that outperform gradient descent. Furthermore, the metalearning procedure automatically avoids overfitting in a principled way, since it punishes overfitting online learners just like it punishes slow ones, simply because overfitters and slow learners cause more cumulative errors during metalearning. Hochreiter himself admits the paper is not well-written. But the results are quite amazing: http://www.cs.colorado.edu/~hochreit @inproceedings{Hochreiter:01meta, author = "S. Hochreiter and A. S. Younger and P. R. Conwell", title = "Learning to learn using gradient descent", booktitle= "Lecture Notes on Comp. Sci. 2130, Proc. Intl. Conf. on Artificial Neural Networks (ICANN-2001)", editors = "G. Dorffner and H. Bischof and K. Hornik", publisher= "Springer: Berlin, Heidelberg", pages = "87-94", year = "2001"} ------------------------------------------------- Juergen Schmidhuber director IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland juergen at idsia.ch www.idsia.ch/~juergen