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Modeling Brain Function

Modeling Brain Function
The World of Attractor Neural Networks

$80.00

  • Date Published: June 1992
  • availability: Available
  • format: Paperback
  • isbn: 9780521421249

$80.00
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About the Authors
  • Exploring one of the most exciting and potentially rewarding areas of scientific research, the study of the principles and mechanisms underlying brain function, this book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. Substantial progress in understanding memory, the learning process, and self-organization by studying the properties of models of neural networks have resulted in discoveries of important parallels between the properties of statistical, nonlinear cooperative systems in physics and neural networks. The author presents a coherent and clear, nontechnical view of all the basic ideas and results. More technical aspects are restricted to special sections and appendices in each chapter.

    Reviews & endorsements

    "...of interest to those following the neural net field...takes off from discoveries that link areas of physics with the emerging neural network paradigm." Intelligence Monthly

    "...regard this book as an opening of a discussion--undoubtedly a very qualified one." Journal of Mathematical Psychology

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    Product details

    • Date Published: June 1992
    • format: Paperback
    • isbn: 9780521421249
    • length: 524 pages
    • dimensions: 229 x 152 x 30 mm
    • weight: 0.76kg
    • contains: 107 b/w illus. 1 table
    • availability: Available
  • Table of Contents

    Preface
    1. Introduction
    2. The basic attractor neural network
    3. General ideas concerning dynamics
    4. Symmetric neural networks at low memory loading
    5. Storage and retrieval of temporal sequences
    6. Storage capacity of ANNs
    7. Robustness - getting closer to biology
    8. Memory data structures
    9. Learning
    10. Hareware implementations of neural networks
    Glossary
    Index.

  • Author

    Daniel J. Amit

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