Overview

The human brain, wi th its hundred billion or more neurons, is both one of the most complex systems known to man and one of the most important. The last decade has seen an explosion of experimental research on the brain, but little theory of neural networks beyond the study of electrical properties of membranes and small neural circuits. Nonetheless, a number of workers in Japan, the United States and elsewhere have begun to contribute to a theory which provides techniques of mathematical analysis and computer simulation to explore properties of neural systems containing immense numbers of neurons. Recently, it has been gradually recognized that rather independent studies of the dynamics of pattern recognition, pattern format::ion, motor control, self-organization, etc. , in neural systems do in fact make use of common methods. We find that a ""competition and cooperation"" type of interaction plays a fundamental role in parallel information processing in the brain. The present volume brings together 23 papers presented at a U. S. -Japan Joint Seminar on ""Competition and Cooperation in Neural Nets"" which was designed to catalyze better integration of theory and experiment in these areas. It was held in Kyoto, Japan, February 15-19, 1982, under the joint sponsorship of the U. S. National Science Foundation and the Japan Society for the Promotion of Science. Participants included brain theorists, neurophysiologists, mathematicians, computer scientists, and physicists. There are seven papers from the U. S.

Full Product Details

Author:   Shun-ichi Amari ,  M. A. Arbib
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Volume:   45
Dimensions:   Width: 17.00cm , Height: 2.30cm , Length: 24.40cm
Weight:   0.794kg
ISBN:  

9783540115748

ISBN 10:   3540115749
Pages:   441
Publication Date:   01 July 1982
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

I. An Opening Perspective.- 1. Competitive and Cooperative Aspects in Dynamics of Neural Excitation and Self-Organization.- II. Reaction-Diffusion Equations.- 2. Sigmoidal Systems and Layer Analysis.- 3. Asymptotic Behavior of Stationary Homogeneous Neuronal Nets.- 4. Aggregation and Segregation Phenomena in Reaction-Diffusion Equations.- III. Single-Neuron and Stochastic Models.- 5. Nerve Pulse Interactions.- 6. Micronetworks in Nerve Cells.- 7. Role and Use of Noise in Biological Systems.- 8. Stochastic, Quantal Membrane Conductances and Neuronal Function.- 9. Diffusion Approximations and Computational Problems for Single Neurons' Activity.- 10. Periodic Pulse Sequences Generated by an Analog Neuron Model.- 11. On a Mathematical Neuron Model.- IV. Oscillations in Neural Networks.- 12. Control of Distributed Neural Oscillators.- 13. Characteristics of Neural Network with Uniform Structure.- V. Development and Plasticity of the Visual Systems.- 14. Systems Matching and Topographic Maps: The Branch-Arrow Model (BAM).- 15. Differential Localization of Plastic Synapses in the Visual Cortex of the Young Kitten: Evidence for Guided Development of the Visual Cortical Networks.- 16. Self-Organization of Neural Nets with Competitive and Cooperative Interaction.- 17. A Simple Paradigm for the Self-Organized Formation of Structured Feature Maps.- 18. Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition.- 19. On the Spontaneous Emergence of Neuronal Schemata.- 20. Associative and Competive Principles of Learning and Development.- VI. Sensori-Motor Transformations and Learning.- 21. Modelling Neural Mechanisms of Visuomotor Coordination in Frog and Toad.- 22. Two-Dimensional Model of Retinal-Tectal-Pretectal Interactions for the Control of Prey-Predator Recognition and Size Preference in Amphibia.- 23. Tensor Theory of Brain Function:The Cerebellum as a Space-Time Metric.- 24. Mechanisms of Motor Learning.- 25. Dynamic and Plastic Properties of the Brain Stem Neuronal Networks as the Possible Neuronal Basis of Learning and Memory.

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