Dynamics and Learning in Recurrent Neural Networks

Call for Papers

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.

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 behavior 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 feed forward neural networks are well understood, our understanding of recurrent neural network is still rather rudimentary. This special issue of Advances in Artificial Neural Systems will highlight the current state of the knowledge on how to configure the connectivity of the artificial neural networks to produce a desired dynamic behavior. 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 this special issue includes discrete and continuous state networks in simulation and hardware realization. Specific topics include, but are not limited to:

Authors should follow the Advances in Artificial Neural Systems manuscript format described at the journal website http://www.hindawi.com/journals/aans/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/, according to the following timetable:

Manuscript Due	April 1, 2008
First Round of Reviews	July 1, 2008
Publication Date	October 1, 2008

Guest Editor

• Akira Imada, Brest State Technical University, 224017 Brest, Belarus