Modeling memory: what do we learn from attractor neural networks?
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.
Laboratoire de physique statistique de l'ENS (laboratoire associé au CNRS-Ura 1306 et aux universités Paris-VI et Paris-VII), Ecole normale supérieure, France.
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Brunel
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Nadal
D006801:Humans; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D008960:Models, Psychological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology