Neural Network Modeling: Statistical Mechanics and Cybernetic Perspectives

ISBN-13: 9780849324888 / Angielski / Twarda / 1994 / 256 str.

Perambur S. Neelakanta; P. S. Neelakanta; Dolores Degroff

Opis

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.

Spis treści

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

O autorze

Neelakanta, P. S.; DeGroff, Dolores