Parallel Models of Associative Memory / Edition 1

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This update of the 1981 classic on neural networks includes new commentaries by the authors that show how the original ideas are related to subsequent developments. As researchers continue to uncover ways of applying the complex information processing abilities of neural networks, they give these models an exciting future which may well involve revolutionary developments in understanding the brain and the mind — developments that may allow researchers to build adaptive intelligent machines. The original chapters show where the ideas came from and the new commentaries show where they are going.

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Product Details

  • ISBN-13: 9780805802702
  • Publisher: Taylor & Francis
  • Publication date: 1/1/1989
  • Series: Cognitive Science Series
  • Edition description: Updated ed
  • Edition number: 1
  • Pages: 352
  • Lexile: 1370L (what's this?)

Table of Contents

Contents: G.E. Hinton, J.A. Anderson, Introduction to the Updated Edition. D.E. Rumelhart, D.A. Norman, Introduction. J.A. Anderson, G.E. Hinton, Models of Information Processing in the Brain. J.A. Feldman, A Connectionist Model of Visual Memory. D. Willshaw, Holography, Associative Memory, and Inductive Generalization. T. Kohonen, E. Oja, P. Lehtiö, Storage and Processing of Information in Distributed Associative Memory Systems. S.E. Fahlman, Representing Implicit Knowledge. G.E. Hinton, Implementing Semantic Networks in Parallel Hardware. T.J. Sejnowski, Skeleton Filters in the Brain. J.A. Anderson, M.C. Mozer, Categorization and Selective Neurons. S. Geman, Notes on a Self-Organizing Machine. R. Ratcliff, Parallel-Processing Mechanisms and Processing of Organized Information in Human Memory.

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