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Neurobiology of Neural Networks

Overview

This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists to participate in constructing the next generation of neural networks. Individual chapters were commissioned from selected authors to bridge the gap between present neural network models and the needs of neurophysiologists who are trying to use these models as part of their research...

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Overview

This timely overview and synthesis of recent work in both artificial neural networks and neurobiology seeks to examine neurobiological data from a network perspective and to encourage neuroscientists to participate in constructing the next generation of neural networks. Individual chapters were commissioned from selected authors to bridge the gap between present neural network models and the needs of neurophysiologists who are trying to use these models as part of their research on how the brain works.Daniel Gardner is Professor of Physiology and
Biophysics at Cornell University Medical College.Contents: Introduction: Toward
Neural Neural Networks, Daniel Gardner. Two Principles of Brain Organization: A
Challenge for Artificial Neural Networks, Charles F. Stevens. Static Determinants of
Synaptic Strength, Daniel Gardner. Learning Rules From Neurobiology, Douglas A.
Baxter and John H. Byrne. Realistic Network Models of Distributed Processing in the
Leech, Shawn R. Lockery and Terrence J. Sejnowski. Neural and Peripheral Dynamics as
Determinants of Patterned Motor Behavior, Hillel J. Chiel and Randall D. Beer.
Dynamic Neural Network Models of Sensorimotor Behavior, Eberhard E. Fetz.

The MIT Press

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Editorial Reviews

Booknews
An overview and synthesis of recent work in both artificial neural networks and neurobiology that seeks to examine neurobiological data from a network perspective and to encourage neuroscientists to participate in constructing the next generation of neural networks. Individual chapters were commissioned from selected authors to bridge the gap between present neural network models and the needs of neurophysiologists who are trying to use these models as part of their research on how the brain works. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780262517126
  • Publisher: MIT Press
  • Publication date: 8/2/1993
  • Series: Computational Neuroscience
  • Pages: 241
  • Product dimensions: 7.10 (w) x 10.00 (h) x 0.80 (d)

Meet the Author

Marc Williams is Professor in the School of Social Sciences and International Studies at the University of New South Wales.

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Table of Contents

Contributors
Preface
1 Introduction: Toward Neural Neural Networks 1
2 Two Principles of Brain Organization: a Challenge for Artificial Neural Networks 13
3 Static Determinants of Synaptic Strength 21
4 Learning Rules from Neurobiology 71
5 Realistic Network Models of Distributed Processing in the Leech 107
6 Neural and Peripheral Dynamics as Determinants of Patterned Motor Behavior 137
7 Dynamic Neural Network Models of Sensorimotor Behavior 165
References 191
Index 219
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