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Gail A. Carpenter - Pattern recognition by self-organizing neural networks

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Pattern Recognition by Self-Organizing Neural Networks presents the most recent advances in an area of research that is becoming vitally important in the fields of cognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19 articles take up developments in competitive learning and computational maps, adaptive resonance theory, and specialized architectures and biological connections.Introductory survey articles provide a framework for understanding the many models involved in various approaches to studying neural networks. These are followed in Part 2 by articles that form the foundation for models of competitive learning and computational mapping, and recent articles by Kohonen, applying them to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designing adaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks, selforganizing pattern recognition systems whose top-down template feedback signals guarantee their stable learning in response to arbitrary sequences of input patterns. In Part 4, articles describe embedding ART modules into larger architectures and provide experimental evidence from neurophysiology, event-related potentials, and psychology that support the prediction that ART mechanisms exist in the brain.Gail A. Carpenter is Professor of Mathematics and Cognitive and Neural Systems at Boston University, where Stephen Grossberg is Wang Professor of Cognitive and Neural Systems and Director of the Center for Adaptive Systems. Together they direct the universitys Cognitive and Neural Systems Program.Contributors: J.-P. Banquet, G. A. Carpenter, S. Grossberg, R. Hecht-Nielsen, T. Kohonen, B. Kosko, T. W. Ryan, N. A. Schmajuk, W. Singer, D. Stork, C. von der Malsburg, C. L. Winter.

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title Pattern Recognition By Self-organizing Neural Networks author - photo 1

title:Pattern Recognition By Self-organizing Neural Networks
author:Carpenter, Gail A.
publisher:MIT Press
isbn10 | asin:0262031760
print isbn13:9780262031769
ebook isbn13:9780585038735
language:English
subjectPattern recognition systems, Neural networks (Computer science)
publication date:1991
lcc:TK7882.P3P394 1991eb
ddc:006.4
subject:Pattern recognition systems, Neural networks (Computer science)
Pattern Recognition By Self-Organizing Neural Networks
edited by
Gail A. Carpenter and Stephen Grossberg
A Bradford Book
The MIT Press
Cambridge, Massachusetts
London, England
1991 Massachusetts Institute of Technology
All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.
This book was printed and bound in the United States of America.
Library of Congress Cataloging-in-Publication Data
Pattern recognition by self-organizing neural networks / edited by Gail A. Carpenter and
Stephen Grossberg.
p. cm.
"A Bradford Book."
Includes bibliographical references and indexes.
ISBN 0-262-03176-0
1. Pattern recognition systems. 2. Neural networks (Computer science) I. Carpenter,
Gail A. II. Grossberg, Stephen, 1939
TK7882.P3P394 1991
006.4dc20Picture 2Picture 3Picture 4Picture 590-29071
Picture 6Picture 7Picture 8Picture 9Picture 10CIP
DEDICATION
For Deborah,
Our favorite self-organizer,
With love.
Page v
Table of Contents
List of Authors
Editorial Preface
Part I: Overviews
Chapter 1
Neural network models for pattern recognition and associative memory
Gall A. Carpenter
1
Chapter 2
Nonlinear neural networks: Principles, mechanisms, and architectures
Stephen Grossberg
35
Part II: Competitive Learning And Computational Maps
Chapter 3
Neural pattern discrimination
Stephen Grossberg
111
Chapter 4
Neural expectation: Cerebellar and retinal analogs of cells fired by learnable or unlearned pattern classes
Stephen Grossberg
157
Chapter 5
Self-organization of orientation sensitive cells in the striate cortex
Christoph yon der Malsburg
179
Chapter 6
Adaptive pattern classification and universal recoding, I: Parallel development and coding of neural feature detectors
Stephen Grossberg
207
Chapter 7
The "neural" phonetic typewriter
Teuvo Kohonen
237
Chapter 8
Counterpropagation networks
Robert Hecht-Nielsen
261

Page vi
Part III: Adaptive Resonance Theory
Chapter 9
Adaptive pattern classification and universal recoding, II: Feedback, expectation, olfaction, and illusions
Stephen Grossberg
277
Chapter 10
A massively parallel architecture for a self-organizing neural pattern recognition machine
Call A. Carpenter and Stephen Grossberg
313
Chapter 11
Variations on adaptive resonance
T.W. Ryan and C.L. Winter
383
Chapter 12
ART 2: Self-organization of stable category recognition codes for analog input patterns
Gall A. Carpenter and Stephen Grossberg
397
Chapter 13
Adaptive bidirectional associative memories
Bart Kosko
425
Chapter 14
ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures
Gall A. Carpenter and Stephen Grossberg
451
Chapter 15
ARTMAP: Supervised real-time learning and classification of nonstationary data by a self-organizing neural network
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