Models of Neural Networks III

. Ed(s): Domany, Eytan; Van, Hemmen J. L.; Schulten, K. (Beckmann Institute for Advanced Science and Technology, Urband, Illinois, USA)

ISBN 10: 0387943684 / ISBN 13: 9780387943688

Published by Springer-Verlag New York Inc., 1995

About this Item

Presents a collection of articles by leading researchers in neural networks. This work focuses on data storage and retrieval, and the recognition of handwriting. Editor(s): Domany, Eytan; Van, Hemmen J. L.; Schulten, K. (Beckmann Institute for Advanced Science and Technology, Urband, Illinois, USA). Series: Physics of Neural Networks. Num Pages: 324 pages, biography. BIC Classification: UYQN. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 19. Weight in Grams: 636. . 1995. Hardback. . . . .

Seller Inventory # V9780387943688

Bibliographic Details

• Title: Models of Neural Networks III
• Publisher: Springer-Verlag New York Inc.
• Publication Date: 1995
• Language: English
• ISBN 10: 0387943684
• ISBN 13: 9780387943688
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.