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Memory and Learning in a Class of Neural Network Models

  • D. J. Wallace
Part of the NATO ASI Series book series (NSSB, volume 140)

Abstract

Neural networks are massively parallel computational models which attempt to capture the “intelligent” processing faculties of the nervous system. They have been studied extensively for more than thirty years [1]. Apart from the longer term goal of understanding the nervous system, the current upsurge of interest in such models is driven by at least three factors. First, seminal papers by Hopfield [2] and by Hinton, Rumelhardt, Sejnowski and collaborators [3] exposed many salient properties of the models and extended their richness and potential in a significant way. Second, the developments in the theory of spin-glasses [4] and the discovery of replica symmetry breaking [5] in the long-range Sherrington-Kirkpatrick model [6] have led to an understanding in some depth of the Hopfield model [7]. Finally, there is now the expectation that the implementation of neural network models using VLSI technology may lead to significant computational hardware for a number of image and signal processing applications and for optimisation problems.

Keywords

Firing Pattern Synaptic Connection Finite Size Effect Training Cycle Nominal Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1986

Authors and Affiliations

  • D. J. Wallace
    • 1
  1. 1.Physics DepartmentThe University of EdinburghEdinburghUK

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