Specialization, Adaptation and Optimization in Dilute Random Attractor Neural Networks

  • D. Sherrington
  • K. Y. M. Wong
Part of the NATO ASI Series book series (NSSB, volume 263)


In a biological context one is used to examples in which the properties of organisms are adapted to provide efficient operation in their particular environments, with otherwise similar systems differing in appropriate details when their environments are different. In this talk we demonstrate a similar phenomenon in some simple neural networks. In particular, we consider the performance of dilute attractor neural networks for associative memory with respect to optimal performances as characterized by two different measures, retrieval overlap and size of the basin from which retrieval is possible. We believe however that our conclusions are more broadly applicable1.


Adaptive Network Retrieval Phase Stable Fixed Point Discontinuous Transition Schematic Plot 
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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  1. 1.
    K. Y. M. Wong and D. Sherrington, Optimally adapted attractor neural network in the presence of noise, J. Phys. A in press (1990).Google Scholar
  2. 2.
    B. Derrida, E. Gardner and A. Zippelius, An exactly solvable asymmetric neural network model, Europhys. Lett. 4: 167 (1987).ADSCrossRefGoogle Scholar
  3. 3.
    D. Amit, M. Evans, H. Horner and K. Y. M. Wong, Retrieval phase diagrams for attractor neural networks with optimal interactions, J. Phys. A 23: 3361 (1990).ADSzbMATHCrossRefGoogle Scholar
  4. 4.
    K. Y. M. Wong and D. Sherrington, Training noise adaptation in attractor neural networks, J. Phys. A 23: L175 (1990).ADSzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media 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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