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:oShannon information and information dynamicsoneural complexity as an information processing systemomemory and information storage in the interconnected neural weboextremum (maximum and minimum) information entropyoneural network trainingonon-conventional, statistical distance-measures for neural network optimizationsosymmetric and asymmetric characteristics of information-theoretic error-metricsoalgorithmic complexity based representation of neural information-theoretic parametersogenetic algorithms versus neural informationodynamics of neurocybernetics viewed in the information-theoretic planeononlinear, information-theoretic transfer function of the neural cellular unitsostatistical mechanics, neural networks, and information theoryosemiotic framework of neural information processing and neural information flowofuzzy information and neural networksoneural dynamics conceived through fuzzy information parametersoneural information flow dynamicsoinformatics of neural stochastic resonanceInformation-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.