Neural Network Modeling: Statistical Mechanics and Cybernetic Perspectives

by P. S. Neelakanta, Dolores DeGroff

Overview

Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions.

Product Details

ISBN-13:	9780849324888
Publisher:	Taylor & Francis
Publication date:	07/12/1994
Edition description:	New Edition
Pages:	256
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

Neelakanta, P. S.; DeGroff, Dolores

Table of Contents

Introduction. Neural and Brain Complex. Concepts of Mathematical Neurobiology. Pseudo-Thermodynamics of Neural Activity. The Physics of Neural Activity: A Statistical Mechanics Perspective. Stochastic Dynamics of the Neural Complex. Neural Field Theory: Quasiparticle Dynamics and Wave Mechanics Analogies of Neural Networks. Informatic Aspects of Neurocybernetics. Appendices: Magnetism and the Ising Spin-Glass Model. Matrix Methods in Little's Model. Overlap of Replicas and Replica Symmetry. Bibliography. Index.