neural net intro books

[Dave.Touretzky@B.GP.CS.CMU.EDU]

A common topic of discussion when academic neural net types get together is
``What sort books are available for teaching neural nets?''  I recently got
a catalog from Van Nostrand Reinhold that listed two introductory books,
although they're not exactly textbooks.  The details are given below.  I
haven't seen either of them yet, so this is not an endorsement, just an
announcement of their existence.  If someone has seen these books and would
like to post a short review to CONNECTIONISTS, that would be helpful.

-- Dave

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NEURAL COMPUTING

Theory and Practice by Philip D. Wasserman, ANZA Research, Inc.
230 pages, 100 illustrations, $36.95

The complex mathematics and algorithms of artificial neural networks are
broken down into simple procedures in this welcome tutorial.  Fully explored
are network fundamentals, implementation of commonly-used paradigms, and how
to enhance problem-solving through integration of neural net research with
traditional artificial intelligence and computing methods.  Real-world
examples clarify applications of artificial neural networks in computer
science, engineering, physiology, and psychology.

CONTENTS:  Introduction.  Fundamentals of Artificial Neural Networks.
Perceptrons.  Backpropagation.  Counterpropagation Networks.  Statistical
Methods.  Hopfield Nets.  Bidirectional Associative Memories.  Adaptive
Resonance Theory.  Optical Neural Networks.  The Cognitron and Neocognitron.
APPENDICES.  The Biological Neural Network.  Vector and Matrix Operations.
Training Algorithms.  Index.


NEURAL NETWORK ARCHITECTURES

An Introduction by Judith Dayhoff, Ph.D., Editor of the Journal of Neural
Network Computing
220 pages, 100 illustrations, $34.95

This down-to-earth book gives you a plain-English explanation of the
relationships between biological and artificial neural networks, plus
detailed assessments of important uses of network architectures today.

CONTENTS:  An Overview of Neural Network Technology.  Neurons and Network
Topologies.  Early Paradigms - The Beginnings of Today's Neural Networks.
The Hopfield Network: Computational Models and Result.  Back-Error
Propagation: Paradigms and Applications.  Competitive Learning: Paradigms
and Competitive Neurons from Biological Systems.  Biological Neural Systems:
Organization.  Structural Diversity.  Temporal Dynamics.  Origins of
Artificial Neural Systems.  Brain Structure and Function.  Biological Nerve
Cells.  Synapses - How Do Living Nerve Cells Interconnect?  What Is Random
and What Is Fixed in the Brain's Neural Networks?  How Do Biological
Systems Really Compare to Computational Neural Networks?  Associative and
Adaptive Networks - More Paradigms.  More Applications - Emphasizing Vision,
Speech, and Pattern Recognition.  New Directions for Neural Networks.