Models of Neural Networks III: Association, Generalization, and Representation (Physics of Neural Networks)

ISBN 10: 1461268826 ISBN 13: 9781461268826

Editorial: Springer, 2012

Idioma: Ingl�s

Condici�n: Nuevo Encuadernaci�n de tapa blanda

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T�tulo

Models of Neural Networks III: Association, Generalization, and Representation (Physics of Neural Networks)

Editor

Springer

Fecha de publicaci�n

2012

Idioma

Ingl�s

ISBN 10

1461268826

ISBN 13

9781461268826

Encuadernaci�n

Encuadernaci�n de tapa blanda

Estado

New

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Sinopsis

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.

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