Models of Neural Networks III - Association, Generalization, and Representation (Paperback, Softcover reprint of the original 1st ed. 1996)

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."

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	Physics of Neural Networks
Release date:	December 2012
First published:	1996
Editors:	Eytan Domany • J. Leo Van Hemmen • Klaus Schulten
Dimensions:	235 x 155 x 17mm (L x W x T)
Format:	Paperback
Pages:	311
Edition:	Softcover reprint of the original 1st ed. 1996
ISBN-13:	978-1-4612-6882-6
Categories:	Books > Science & Mathematics > Physics > Thermodynamics & statistical physics > Statistical physics
LSN:	1-4612-6882-6
Barcode:	9781461268826

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