Information-theoretic aspects of neural networks

著者

• Neelakanta, Perambur S.

書誌事項

Information-theoretic aspects of neural networks

edited by Perambur S. Neelakanta

CRC Press, c1999

注記

Includes bibliographic references (p. 395-396) and index

内容説明・目次

内容説明

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.

目次

IntroductionNeural Complex: A Nonlinear CI System?Neural Complex vis-a-vis Statistical Mechanics, Entropy, Thermodynamics and Information TheoryNeural Communication and Control in Information-Theoretic PlaneNeural Complexity: An Algorithmic RepresentationNeural Information DynamicsSemiotic Framework of Neural Information ProcessingGenetic Algorithmic Based Depiction of Neural InformationEpilogueAppendix

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BA41627359
• ISBN:
  • 0849331986
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Boca Raton, Fla.
• ページ数/冊数: 402 p.
• 大きさ: 25 cm
• 分類:
  • LCC : QA76.87
  • DC21 : 006.3/2
  • DC21 : 006.32
• 件名:
  • LCSH : Neural networks (Computer science)
  • LCSH : Information science