Dynamic Interactions in Neural Networks: Models and Data

1. Front Matter

   Pages i-viii

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2. Dynamic Interactions in Neural Networks: An Introductory Perspective

   1. Dynamic Interaction in Neural Networks: An Introductory Perspective

      • Michael A. Arbib

      Pages 1-11

3. Development and Learning in Adaptive Networks

   1. Front Matter

      Pages 13-13

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   2. Dynamical Stability of Formation of Cortical Maps

      • Shun-ichi Amari

      Pages 15-34

   3. Visual Plasticity in the Auditory Pathway: Visual Inputs Induced into Auditory Thalamus and Cortex Illustrate Principles of Adaptive Organization in Sensory Systems

      • Mriganka Sur

      Pages 35-51

   4. The Hippocampus and the Control of Information Storage in the Brain

      • Nestor A. Schmajuk

      Pages 53-72

   5. A Memory with Cognitive Ability

      • Shigeru Shinomoto

      Pages 73-86

   6. Feature Handling in Learning Algorithms

      • S. E. Hampson, D. J. Volper

      Pages 87-105

   7. Self-Organizing Neural Network with the Mechanism of Feedback Information Processing

      • Sei Miyake, Kunihiko Fukushima

      Pages 107-119

4. Visual Function

   1. Front Matter

      Pages 121-121

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   2. Interacting Subsystems for Depth Perception and Detour Behavior

      • Michael A. Arbib

      Pages 123-151

   3. Role of Basal Ganglia in Initiation of Voluntary Movements

      • Okihide Hikosaka

      Pages 153-167

   4. Neural Mechanisms of Attention in Extrastriate Cortex of Monkeys

      • Robert Desimone, Jeffrey Moran, Hedva Spitzer

      Pages 169-182

   5. Neuronal Representation of Pictorial Working Memory in the Primate Temporal Cortex

      • Yasushi Miyashita

      Pages 183-191

5. Motor Control and the Cerebellum

   1. Front Matter

      Pages 193-193

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   2. Hierarchical Learning of Voluntary Movement by Cerebellum and Sensory Association Cortex

      • Mitsuo Kawato, Michiaki Isobe, Ryoji Suzuki

      Pages 195-214

   3. A Model for Oblique Saccade Generation and Adaptation

      • Masahiko Fujita

      Pages 215-226

   4. Cerebellar Mechanisms in the Adaptation of Vestibuloocular Reflex

      • Yasushi Miyashita, Koichi Mori

      Pages 227-237

   5. A Kalman Filter Theory of the Cerebellum

      • Michael Paulin

      Pages 239-259

   6. Conditioning and the Cerebellum

      • John W. Moore, Diana E. J. Blazis

      Pages 261-277

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.

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

• Center for Neural Engineering, University of Southern California, Los Angeles, USA

  Michael A. Arbib

• Department of Mathematical Engineering and Instrumentation Physics, University of Tokyo, Tokyo, Japan

  Shun-ichi Amari

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