1st Edition

Neural Network Modeling Statistical Mechanics and Cybernetic Perspectives

By P. S. Neelakanta, Dolores DeGroff Copyright 1994

    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.

    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


    Neelakanta, P. S.; DeGroff, Dolores