The Handbook of Brain Theory and Neural Networks

[Michael A. Arbib]

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from Michael A. Arbib: arbib at pollux.usc.edu

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The following is adapted from the brochure put out by MIT Press
for The Handbook of Brain Theory and Neural Networks which
they have just published, and which I edited:


"In hundreds of articles by experts from around the world, and in
overviews and "road maps" prepared by the editor, the Handbook of Brain
Theory and Neural Networks charts the immense progress made in recent
years in many specific topics related to two great questions: How does
the brain work? and How can we build intelligent machines?
 
"While many books have appeared on limited aspects of one subfield or
another of brain theory and neural networks, this handbook covers the
entire sweep of topics - from detailed models of single neurons,
analyses of a wide variety of biological neural networks, and
connectionist studies, to mathematical analyses of a variety of abstract
neural networks, and technological applications of adaptive, artificial
neural networks.
 
"The excitement, and the frustration, of these topics is that they span
such a broad range of disciplines including mathematics, statistical
physics and chemistry, neurology and neurobiology, and computer science
and electrical engineering as well as cognitive psychology, artificial
intelligence, and philosophy. Thus, much effort has gone into making the
Handbook accessible to readers with varied backgrounds while still
providing a clear view of much of the recent, specialized research in
specific topics.


PART II - ROAD MAPS provides an entree into the many articles 
ds while still
providing a clear view of much of the recent, specialized research in
specific topics.
 
"The heart of the book, Part III - ARTICLES, is comprised
of 266 original articles by leaders in the various fields, arranged
alphabetically by title.    Parts I and II, written by the editor, 
are designed to help readers orient themselves to this vast range of material. 

PART I - BACKGROUND introduces several basic neural models, 
explains how the present study of Brain Theory and  Neural 
Networks integrates brain theory, artificial intelligence, and 
cognitive psychology, and provides a tutorial on the concepts 
essential for understanding neural networks as dynamic, adaptive 
systems. 

PART II - ROAD MAPS provides an entree into the many articles 
of Part III via an introductory "Meta-Map" and twenty-thrRONS AND NETWORKS
Biological Neurons
Biological Networks
Mammalian Brain Regions

SENSORY SYSTEMS
Vision
Other Sensory Systems

rouped under eight general headings:

CONNECTIONISM: PSYCHOLOGY, LINGUISTICS, AND AI
Connectionist Psychology
Connectionist Linguistics
Artificial Intelligence and Neural Networks

DYNAMICS, SELF-ORGANIZATION, AND COOPERATIVITY
Dynamic Systems and Optimization
Cooperative Phenomena
Self-Organization in Neural Networks

LEARNING IN ARTIFICIAL NEURAL NETWORKS
Learning in Artificial Neural Networks, Deterministic
Learning in Artificial Neural Networks, Statistical
Computability and Complexity

APPLICATIONS AND IMPLEMENTATIONS
Control Theory and Robotics
Applications of Neural Networks
Implementation  of Neural Networks

BIOLOGICAL NEURONS AND NETWORKS
Biological Neurons
Biological Networks
Mammalian Brain Regions

SENSORY SYSTEMS
Vision
Other Sensory Systems

PLASTICITY IN DEVELOPMENT AND LEARNING
Mechanisms of Neural Plasticity
Development and Regeneration of Neural Networks
Learning in Biological Systems

MOTOR CONTROL
Motor Pattern Generators and Neuroethology
Biological Motor Control
Primate Motor Control

*****

REQUEST FOR FEEDBACK

Any feedback on the Handbook would be much appreciated - both praise
for what worked well and suggestions for what needs improvement.

In particular, what topics are missing in Part III (you'll need to tour the
Index of the Handbook as well as looking for the "obvious" headings in
the Table of Contents), and what subtopics are missing in individual articles?

And how might Parts I and II be improved?

Any other comments and suggestions will be most welcome.  Meanwhile, I 
hope that many of you enjoy the book and find that it does succeed in
bridging the cultural divide between those who study the brain and those
who study artificial neural networks.

*****

With best wishes

Michael Arbib


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