Connectionists: CFP Neural Networks Special Issue on ESNs and LSMs

[Herbert Jaeger]

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CALL FOR PAPERS: Neural Networks 2007 Special Issue

"Echo State Networks and Liquid State Machines"

Guest Co-Editors :

Dr. Herbert Jaeger, International University Bremen,
h.jaeger at iu-bremen.de
Dr. Wolfgang Maass, Technische Universitaet Graz,
maass at igi.tugraz.at
Dr. Jose C. Principe, University of Florida,
principe at cnel.ufl.edu


A new approach to analyzing and training recurrent neural network
(RNNs) has emerged over the last few years. The central idea is to
regard a RNN as a nonlinear, excitable medium, which is driven by
input signals or fed-back output signals. From the excited response
signals inside the medium, simple (typically linear), trainable
readout mechanisms distil the desired output signals. The medium
consists of a large, randomly connected network, which is not adapted
during learning. It is variously referred to as a dynamical reservoir
or liquid. There are currently two main flavours of such
networks. Echo state networks were developed from a mathematical and
engineering background and are composed of simple sigmoid units,
updated in discrete time. Liquid state machines were conceived from a
mathematical and computational neuroscience perspective and usually
are made of biologically more plausible, spiking neurons with a
continuous-time dynamics. These approaches have quickly gained
popularity because of their simplicity, expressiveness, ease of
training and biological appeal.

This Special Issue aims at establishing a first comprehensive overview
of this newly emerging area, demonstrating the versatility of the
approach, its mathematical foundations and also its limitations.
Submissions are solicited that contribute to this area of research
with respect to

--	mathematical and algorithmic analysis,
--	biological and cognitive modelling,
--	engineering applications,
--	toolboxes and hardware implementations.

One of the main questions in current research in this field concerns
the structure of the dynamical reservoir / liquid. Submissions are
especially welcome which investigate the relationship between the
excitable medium topology and algebraic properties and the resulting
modeling capacity, or methods for pre-adapting the medium by
unsupervised or evolutionary mechanisms, or including special-purpose
sub networks (as for instance, feature detectors) into the medium.

Submission of Manuscript

The manuscripts should be prepared according
to the format of the Neural Networks and electronically submitted to
one of the Guest Editors. The review will take place within 3 months
and only very minor revisions will be accepted. For any further
question, please contact the Guest Editors.

DEADLINE FOR SUBMISSION : June 1, 2006.


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Dr. Herbert Jaeger

Professor for Computational Science
International University Bremen
Campus Ring 12
28759 Bremen, Germany

Phone (+49) 421 200 3215
Fax (+49) 421 200 49 3215
email  h.jaeger at iu-bremen.de

http://www.faculty.iu-bremen.de/hjaeger/
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