Connectionists: Reminder: Deadline approaching Speciai Issue: Dynamics and learning in Recurrent Neural Networks

[Joaquin Sitte]

Deadline approaching!

Special issue of Advances in Artificial Neural Systems on  http://www.hindawi.com/journals/aans



Dynamics and Learning in Recurrent Neural Networks



Advances in Artificial Neural Systems welcomes original research papers and authorative reviews for a special issue on Dynamics and Learning in Recurrent Neural Networks scheduled for publication in October 2008.



Deadline for submissions: 1 April 2008.

First round of review: 1 July 2008

Tentative Publication: 1 October 2008



Feedback connections are ubiquitous in natural neural system, especially within the layers of the neocortex. From mathematical models of networks of simple neural elements with feedback connections we know that these can in principle reproduce the behaviour of almost any dynamical system. These networks can exhibit from limit point to strange attractor dynamics and have been found to be able to perform useful functions such as associative memory, pattern recognition, optimization, central pattern generators and more.



While feedforward neural Networks are well understood, our understanding of recurrent neural network is still rather rudimentary. This special issue of Advances in Artificial Neural System will highlight the current state of the knowledge on how to configure the connectivity of the artificial neural networks to produce a desired dynamic behaviour.

In this context, the discovery of learning methods, understood as the automatic configuration driven by sensor inputs, is a primordial objective recurrent neural network research. The scope of his special issue includes discrete and continuous state networks in simulation and hardware realization. Specific topics include, but are not limited to,  Attractor Networks, Cooperative-Competitive networks, Recurrent Support Vector Machines, Echo-state and Liquid-state networks,  Bayesian Inference and Belief Networks,  Patchy cortical networks,  Spike-based plasticity in recurrent networks

and neuromorphic implementations of recurrent networks.



Dr. Joaquin Sitte,  Associate Professor.

School of Software Engineering and Data Communication Faculty of Information Technology

Queensland University of Technology

GPO Box 2434, Brisbane, Q 4001 Australia

Phone +61 7 3138 2755

Fax +61 7 3138 1801

e-mail: j.sitte at qut.edu.au

homepage http://www.fit.qut.edu.au/~sitte







Dr. Joaquin Sitte,  Associate Professor.
 Faculty of Information Technology
Queensland University of Technology
GPO Box 2434, Brisbane, Q 4001 Australia
Phone +61 7 3138 2755
Fax +61 7 3138 1801
e-mail: j.sitte at qut.edu.au<mailto:j.sitte at qut.edu.au>
homepage http://www.fit.qut.edu.au/~sitte



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