Models of Neural Networks III: Association, Generalization, and Representation (Physics of Neural Networks) - Image 1

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

Name: Models of Neural Networks III: Association, Generalization, and Representation (Physics of Neural Networks)
Brand: Springer
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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.

Product Specifications

Brand: Springer
Format: hardcover
ASIN: 0387943684
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 01 December 1995
Listed Since: 13 February 2007

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

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