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Neural network modeling of associative memory: Beyond the Hopfield model

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  • Dasgupta, Chandan

Abstract

A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

Suggested Citation

  • Dasgupta, Chandan, 1992. "Neural network modeling of associative memory: Beyond the Hopfield model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 186(1), pages 49-60.
    • Handle: RePEc:eee:phsmap:v:186:y:1992:i:1:p:49-60
    DOI: 10.1016/0378-4371(92)90364-V
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    1. Colman, David & Leech, Dennis, 1969. "Structural Change in the Dairy Industry In England and Values An Application of Markov Chain Analysis," Bulletins 232765, University of Manchester, School of Economics, Agricultural Economics Department.
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