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Synopsis
Pattern Recognition by Self-Organizing Neural Networks presents the most recent advances in an area of research that is becoming vitally important in the fields of cognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19 articles take up developments in competitive learning and computational maps, adaptive resonance theory, and specialized architectures and biological connections.
Introductory survey articles provide a framework for understanding the many models involved in various approaches to studying neural networks. These are followed in Part 2 by articles that form the foundation for models of competitive learning and computational mapping, and recent articles by Kohonen, applying them to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designing adaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks, selforganizing pattern recognition systems whose top-down template feedback signals guarantee their stable learning in response to arbitrary sequences of input patterns. In Part 4, articles describe embedding ART modules into larger architectures and provide experimental evidence from neurophysiology, event-related potentials, and psychology that support the prediction that ART mechanisms exist in the brain.
Contributors: J.-P. Banquet, G.A. Carpenter, S. Grossberg, R. Hecht-Nielsen, T. Kohonen, B. Kosko, T.W. Ryan, N.A. Schmajuk, W. Singer, D. Stork, C. von der Malsburg, C.L. Winter.
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About the Author
Gail A. Carpenter is Professor of Mathematics and Cognitive and Neural Systems and Director of the CNS Technology Lab at Boston University.
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- PublisherBradford Book
- Publication date1991
- ISBN 10 0262031760
- ISBN 13 9780262031769
- BindingHardcover
- LanguageEnglish
- Number of pages709
- EditorCarpenter Gail A, Grossberg Stephen
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Pattern Recognition by Self-Organizing Neural Networks
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Massachusetts Institute of Technology, 1991
ISBN 10: 0262031760
ISBN 13: 9780262031769
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