Information-Theoretic Aspects of Neural Networks

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.