Information-Theoretic Aspects of Neural Networks

Published:
March 30, 1999 by CRC Press - 416 Pages
Author(s):
P. S. Neelakanta, Florida Atlantic University, Boca Raton, Florida, USA

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ISBN 9780849331985
Cat# 3198
 

Features

  • Presents a focused insight as well as new perspectives on information-processing associated with real and artificial networks
  • Enables the design of better neural network models
  • Provides alternative strategies for designing and understanding the dynamics of more complex neural networks
  • Introduces new cost-functions
  • Contains more 8,000 entries and more than 120 tables and figures
  • Includes exhaustive references - some presented for the first time

    • Summary

    Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural information.
    Information-Theoretic Aspects of Neural Networks cohesively explores this burgeoning discipline, covering topics such as:

  • Shannon information and information dynamics
  • neural complexity as an information processing system
  • memory and information storage in the interconnected neural web
  • extremum (maximum and minimum) information entropy
  • neural network training
  • non-conventional, statistical distance-measures for neural network optimizations
  • symmetric and asymmetric characteristics of information-theoretic error-metrics
  • algorithmic complexity based representation of neural information-theoretic parameters
  • genetic algorithms versus neural information
  • dynamics of neurocybernetics viewed in the information-theoretic plane
  • nonlinear, information-theoretic transfer function of the neural cellular units
  • statistical mechanics, neural networks, and information theory
  • semiotic framework of neural information processing and neural information flow
  • fuzzy information and neural networks
  • neural dynamics conceived through fuzzy information parameters
  • neural information flow dynamics
  • informatics of neural stochastic resonance
    Information-Theoretic Aspects of Neural Networks acts as an exceptional resource for engineers, scientists, and computer scientists working in the field of artificial neural networks as well as biologists applying the concepts of communication theory and protocols to the functioning of the brain. The information in this book explores new avenues in the field and creates a common platform for analyzing the neural complex as well as artificial neural networks.

    • Table of Contents

    Introduction
    Neural Complex: A Nonlinear CI System?
    Neural Complex vis-a-vis Statistical Mechanics, Entropy, Thermodynamics and Information Theory
    Neural Communication and Control in Information-Theoretic Plane
    Neural Complexity: An Algorithmic Representation
    Neural Information Dynamics
    Semiotic Framework of Neural Information Processing
    Genetic Algorithmic Based Depiction of Neural Information
    Epilogue
    Appendix

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