Dynamic Interactions in Neural Networks: Models and Data

  • Michael A. Arbib
  • Shun-ichi Amari

Part of the Research Notes in Neural Computing book series (NEURALCOMPUTING, volume 1)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Dynamic Interactions in Neural Networks: An Introductory Perspective

  3. Development and Learning in Adaptive Networks

  4. Visual Function

    1. Front Matter
      Pages 121-121
    2. Robert Desimone, Jeffrey Moran, Hedva Spitzer
      Pages 169-182
  5. Motor Control and the Cerebellum

    1. Front Matter
      Pages 193-193
    2. Mitsuo Kawato, Michiaki Isobe, Ryoji Suzuki
      Pages 195-214
    3. Yasushi Miyashita, Koichi Mori
      Pages 227-237
    4. Michael Paulin
      Pages 239-259
    5. John W. Moore, Diana E. J. Blazis
      Pages 261-277

About these proceedings

Introduction

This is an exciting time. The study of neural networks is enjoying a great renaissance, both in computational neuroscience - the development of information processing models of living brains - and in neural computing - the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume, Dynamic Interactions in Neural Networks: Models and Data can be given two interpretations. We present models and data on the dynamic interactions occurring in the brain, and we also exhibit the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles that may guide us in the understanding of our own brains and in the design of artificial neural networks. In fact, the book title has yet a third interpretation. It is based on the U. S. -Japan Seminar on "Competition and Cooperation in Neural Nets" which we organized at the University of Southern California, Los Angeles, May 18-22, 1987, and is thus the record of interaction of scientists on both sides of the Pacific in advancing the frontiers of this dynamic, re-born field. The book focuses on three major aspects of neural network function: learning, perception, and action. More specifically, the chapters are grouped under three headings: "Development and Learning in Adaptive Networks," "Visual Function", and "Motor Control and the Cerebellum.

Keywords

Ganglia algorithms artificial intelligence artificial neural network behavior control learning memory neural networks neuroscience organization perception

Editors and affiliations

  • Michael A. Arbib
    • 1
  • Shun-ichi Amari
    • 2
  1. 1.Center for Neural EngineeringUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of Mathematical Engineering and Instrumentation PhysicsUniversity of TokyoTokyoJapan

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-4536-0
  • Copyright Information Springer-Verlag New York 1989
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-96893-3
  • Online ISBN 978-1-4612-4536-0
  • Series Print ISSN 0939-4818
  • About this book