Book on Pulsed Neural Networks

[Wolfgang Maass]

    The following book has just appeared at MIT-Press:

                    PULSED NEURAL NETWORKS
 
    edited by Wolfgang Maass and Christopher M. Bishop

Contributors:
Peter S. Burge, Stephen R. Deiss, Rodney J. Douglas, John G. Elias,
Wulfram Gerstner, Alister Hamilton, David Horn, Axel Jahnke, Richard
Kempter, Wolfgang Maass, Alessandro Mortara, Alan F. Murray, David P. M.
Northmore, Irit Opher, Kostas A. Papathanasiou, Michael Recce,
Barry J. P. Rising, Ulrich Roth, Tim Schönauer, Terrence J. Sejnowski,
John Shawe-Taylor, Max R. van Daalen, J. Leo van Hemmen, Philippe
Venier, Hermann Wagner, Adrian M. Whatley, Anthony M. Zador.


Most artificial neural network models are inspired by models for
biological neural systems where the output of a neuron is encoded
exclusively in its firing rate:
The output of a computational unit in an artificial neural network is a
(static) binary or continuous variable that may be viewed as a
representation (or abstraction) of the current firing rate of a
biological neuron.

In recent years, however, data from neurobiological experiments have
made it increasingly clear that biological neural networks, which
communicate through pulses (called action potentials or spikes), also
use the timing of these pulses to transmit information and to perform
computation. This realization has stimulated a significant growth of
research activity in the area of pulsed neural networks ranging from
neurobiological modeling and theoretical analyses, to algorithm
development and hardware implementations.

Obviously quite different theoretical tools and models have to be
developed for this purpose, since almost all traditional computational
models (including most artificial neural network models) are based on
the assumption that the timing of atomic computational events does
not depend in an essential way on the input to the computation (an
example is the common assumption that parallel computation steps
are synchronized, another example is the assumption that their timing
is largely stochastic).

For implementations in novel electronic hardware artificial pulsed
neural networks offer the possibility to create intriguing combinations
of ideas from analog and digital circuits: a pulse has a stereotyped
form, hence it  may be viewed as a digital signal. On the other hand
the timing of a pulse may encode an analog variable. The research
reported in this book is motivated both by the desire to enhance
our understanding of information processing in biological networks, as
well as by the goal of developing new information processing
technologies. 


Our aim in producing this book has been to provide a first comprehensive
treatment of the field of pulsed neural networks, which will be
accessible to researchers from diverse disciplines such as electrical
engineering, signal processing, computer science, physics, and
computational neuroscience. By virtue of its pedagogical emphasis, it
will also find a place in many of the advanced undergraduate and
graduate courses in neural networks now taught in many universities. 

Terrence J. Sejnowski's foreword, "Neural Pulse Coding," presents an
overview of the topic. The first half of the book consists of longer
tutorial articles spanning neurobiology, theory, algorithms, and
hardware. The second half contains a larger number of shorter research
chapters that present more advanced concepts. The contributors use
consistent notation and terminology throughout the book.

408 pp., 195 illus., cloth ISBN 0-262-13350-4 
MIT-Press; A Bradford Book

For further information on this book visit

http://www.cis.tu-graz.ac.at/igi/maass/PNN.html

MIT-Press catalogue:
http://mitpress.mit.edu/promotions/books/MAAPHS99

Amazon bookstore ordering information:
http://www.amazon.com/exec/obidos/ASIN/0262133504/qid%3D916171995/002-1728549-3620249

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Christopher M. Bishop is Senior Researcher at Microsoft Research,
Cambridge,
and Professor of Computer Science at the University of Edinburgh.

Wolfgang Maass is Professor at the Institute for Theoretical Computer
Science, Technische Universität Graz, Austria.