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

N. Brunel (1) , J. P. Nadal (1)

1 LPS - Laboratoire de Physique Statistique de l'ENS

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.

Domaines: Neurosciences [q-bio.NC]

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⟩