Neural Network Modeling: Statistical Mechanics and Cybernetic Perspectives

Published:
July 12, 1994 by CRC Press - 256 Pages
Author(s):
P. S. Neelakanta, Florida Atlantic University, Boca Raton, Florida, USA; Dolores DeGroff, Florida Atlantic University

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ISBN 9780849324888
Cat# 2488
 

Features

  • A cohesive treatment of neural biology and physico-mathematical considerations in neurostochastic perspectives
  • A critical appraisal of the interaction physics pertinent to magnetic spins, and a search for alternative interaction models
  • A view of the complex cellular automata as a self-controlling entity representing a system of cybernetics
  • An analysis of the informatic aspects of neurocybernetic complex

    • Summary

    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.

    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

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