Dynamics of hopfield associative memories

  • M. R. B. Forshaw
Adaptive Learning Networks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 301)


It is unlikely that the Hopfield model will be of much practical use as a stand-alone associative memory for pattern recognition purposes. Its importance lies in its conceptual significance: since 1982 nearly all of the publications in this field have acknowledged the relevance of this model. Several papers have recently described modifications to the Hebb programming rule, to permit the storage of correlated patterns. Such systems are much more likely to be of use for pattern recognition, either as algorithmic models to be run on existing computers or perhaps as small-scale VLSI implementations. There are several factors which are important when assessing the importance of an associative memory. These include its pattern-storage capacity, its algorithmic or hardware complexity, its ability to recognise imperfect input patterns, and its response time. The present paper is intended to make a contribution towards describing the behaviour of the last two factors.


Spin Glass Associative Memory Energy Landscape Associative Recognition Local Energy Minimum 
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

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • M. R. B. Forshaw
    • 1
  1. 1.Department of Physics and AstronomyUniversity College LondonLondon

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