ABSTRACT

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.
  • chapter Chapter 1|69 pages

    Introduction

    ByP.S. Neelakanta

    chapter Chapter 2|42 pages

    Neural Complex: A Nonlinear C3I System?

    ByP.S. Neelakanta, S. Abusalah, D. De Groff

    chapter Chapter 3|38 pages

    Nonlinear and Informatic Aspects of Fuzzy Neural Activity

    ByP.S. Neelakanta, S. Abusalah

    chapter Chapter 4|82 pages

    Csiszár’s Information-Theoretic Error-Measures for Neural Network Optimizations

    ByP.S. Neelakanta, S. Abusalah, J.C. Park

    chapter Chapter 5|38 pages

    Dynamics of Neural Learning in the Information-Theoretic Plane

    ByP.S. Neelakanta

    chapter Chapter 6|33 pages

    Informatic Perspectives of Complexity Measure in Neural Networks

    ByP.S. Neelakanta, J. C. Park, D. De Groff

    chapter Chapter 7|25 pages

    Information-Theoretic Aspects of Neural Stochastic Resonance

    ByJ.C. Park, P.S. Neelakanta

    chapter Chapter 8|66 pages

    Neural Informatics and Genetic Algorithms

    ByP.S. Neelakanta, T.V. Arredondo