Models of Neural Networks III [electronic resource] : Association, Generalization, and Representation /

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.

Bibliographic Details

Main Authors:	Domany, Eytan. editor., Hemmen, J. Leo van. editor., Schulten, Klaus. editor., SpringerLink (Online service)
Format:	Texto biblioteca
Language:	eng
Published:	New York, NY : Springer New York : Imprint: Springer, 1996
Subjects:	Physics., Statistical physics., Dynamical systems., Statistical Physics, Dynamical Systems and Complexity.,
Online Access:	http://dx.doi.org/10.1007/978-1-4612-0723-8
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