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Neural Networks for Perception - 1st Edition - ISBN: 9780127412528, 9781483262796

Neural Networks for Perception

1st Edition

Computation, Learning, and Architectures

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Editor: Harry Wechsler
eBook ISBN: 9781483262796
Imprint: Academic Press
Published Date: 1st November 1991
Page Count: 384
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Neural Networks for Perception, Volume 2: Computation, Learning, and Architectures explores the computational and adaptation problems related to the use of neuronal systems, and the corresponding hardware architectures capable of implementing neural networks for perception and of coping with the complexity inherent in massively distributed computation.

This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The text is organized into two sections. The first section, computation and learning, discusses topics on learning visual behaviors, some of the elementary theory of the basic backpropagation neural network architecture, and computation and learning in the context of neural network capacity. The second section is on hardware architecture. The chapters included in this part of the book describe the architectures and possible applications of recent neurocomputing models. The Cohen-Grossberg model of associative memory, hybrid optical/digital architectures for neorocomputing, and electronic circuits for adaptive synapses are some of the subjects elucidated.

Neuroscientists, computer scientists, engineers, and researchers in artificial intelligence will find the book useful.

Table of Contents

Contents of Volume 1 : Human and Machine Perception



Part III Computation and Learning


III.1 Learning Visual Behaviors

III.2 Nonparametric Regression Analysis Using Self-Organizing Topological Maps

III.3 Theory of the Backpropagation Neural Network

III.4 Hopfield Model and Optimization Problems

III.5 DAM, Regression Analysis, and Attentive Recognition

III.6 Intelligence Code Machine

III.7 Cycling Logarithmically Converging Networks That Flow Information to Behave (Perceive) and Learn

III.8 Computation and Learning in the Context of Neural Network Capacity

Part IV Architectures


IV.1 Competitive and Cooperative Multimode Dynamics in Photorefractive Ring Circuits

IV.2 Hybrid Neural Networks and Algorithms

IV.3 The Use of Fixed Holograms for Massively-Interconnected, Low-Power Neural Networks

IV.4 Electronic Circuits for Adaptive Synapses

IV.5 Neural Network Computations on a Fine Grain Array Processor



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© Academic Press 1992
1st November 1991
Academic Press
eBook ISBN:

About the Editor

Harry Wechsler

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