Modeling memory: what do we learn from attractor neural networks? - Archive ouverte HAL
Article Dans Une Revue Comptes rendus de l’Académie des sciences. Série III, Sciences de la vie Année : 1998

Modeling memory: what do we learn from attractor neural networks?

Résumé

In this paper we summarize some of the main contributions of models of recurrent neural networks with associative memory properties. We compare the behavior of these attractor neural networks with empirical data from both physiology and psychology. This type of network could be used in models with more complex functions.
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Dates et versions

hal-00143853 , version 1 (27-04-2007)

Identifiants

  • HAL Id : hal-00143853 , version 1
  • PUBMED : 9759349

Citer

N. Brunel, J. P. Nadal. Modeling memory: what do we learn from attractor neural networks?. Comptes rendus de l’Académie des sciences. Série III, Sciences de la vie, 1998, 321 (2-3), pp.249-52. ⟨hal-00143853⟩
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