Dynamic Maps and Attractor Phase-Structures in Randomly Dilute Neural Networks

  • D. Sherrington
  • K. Y. M. Wong
Part of the NATO ASI Series book series (NSSB, volume 264)


There is currently much interest in attractor neural networks as idealized models of associative memory in the cerebral cortex of the brain [1]. They consist of simple neurons driving one another non-linearly via connecting pathways with mutually interfering characteristics, leading to non-trivial global dynamics. The objective is to train the characteristics of the individual pathways so as to lead to a set of ‘basins’ in which the global dynamic activity is attracted towards that associated with the patterns which are to be memorized and recallable. For useful memory there should be many attractor basins, each with macroscopic overlap with a single memorized pattern.


Associative Memory Basin Boundary Stable Fixed Point Attractor Basin Unstable Fixed Point 
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 1991

Authors and Affiliations

  • D. Sherrington
    • 1
  • K. Y. M. Wong
    • 1
  1. 1.Department of PhysicsUniversity of OxfordOxfordUK

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