Overview
- Editors:
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Eytan Domany
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Department of Electronics, Weizmann Institute of Science, Rehovot, Israel
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J. Leo Hemmen
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Institut für Theoretische Physik, Technische Universität München, Garching bei München, Germany
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Klaus Schulten
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Department of Physics and Beckman Institute, University of Illinois, Urbana, USA
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Table of contents (8 chapters)
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Front Matter
Pages i-xiii
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- Günther Palm, Friedrich T. Sommer
Pages 79-118
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- Manfred Opper, Wolfgang Kinzel
Pages 151-209
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- I. Guyon, J. Bromley, N. Matić, M. Schenkel, H. Weissman
Pages 255-279
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- Kakali Sarkar, Klaus Schulten
Pages 281-302
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Back Matter
Pages 303-311
About this book
One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, "Global Analysis of Recurrent Neural Net works," by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, "Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns" by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization.
Editors and Affiliations
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Department of Electronics, Weizmann Institute of Science, Rehovot, Israel
Eytan Domany
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Institut für Theoretische Physik, Technische Universität München, Garching bei München, Germany
J. Leo Hemmen
-
Department of Physics and Beckman Institute, University of Illinois, Urbana, USA
Klaus Schulten