Information-Theoretic Aspects of Neural Networks
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This book introduces information theory for studying real and artificial neural complexes. Topics include neural information processing, memory, and storage in neural webs, neural network training, algorithmic representation of neural activity, information dynamics, and the semiotic framework of neural information representation and processing. TOC:Introduction.- Neural Complex: A Nonlinear Cl 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.
Information-Theoretic Aspects of Neural Networks is an exceptional resource for engineers 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. It presents focused insight as well as new perspectives on information-processing as it relates to real and artificial networks. Filled with tables and figures, it provides alternative strategies for designing and understanding complex neural networks, introduces new cost-functions, and explores new avenues in the field. The author also includes exhaustive references, some presented here for the first time.
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