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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Models of Neural Networks III: Association, Generalization, and Representation

Models of Neural Networks III: Association, Generalization, and Representation

Domany, Eytan
Hemmen, J. Leo Van
Schulten, Klaus

Hardcover

Series: Physics of Neural Networks

General ComputersGeneral GardeningPhysics

ISBN10: 0387943684
ISBN13: 9780387943688
Publisher: Springer
Published: Dec 1 1995
Pages: 311
Weight: 1.40
Height: 0.75 Width: 6.14 Depth: 9.21
Language: English
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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