Distributed synaptic weights in a LIF neural network and learning rules - INRIA - Institut National de Recherche en Informatique et en Automatique Accéder directement au contenu
Article Dans Une Revue Physica D: Nonlinear Phenomena Année : 2017

Distributed synaptic weights in a LIF neural network and learning rules

Résumé

Leaky integrate-and-fire (LIF) models are mean-field limits, with a large number of neurons, used to describe neural networks. We consider inhomogeneous networks structured by a connec-tivity parameter (strengths of the synaptic weights) with the effect of processing the input current with different intensities. We first study the properties of the network activity depending on the distribution of synaptic weights and in particular its discrimination capacity. Then, we consider simple learning rules and determine the synaptic weight distribution it generates. We outline the role of noise as a selection principle and the capacity to memorized a learned signal.
Fichier principal
Vignette du fichier
distrib_connec_HAL.pdf (449.4 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01541093 , version 1 (17-06-2017)

Identifiants

Citer

Benoît Perthame, Delphine Salort, Gilles Wainrib. Distributed synaptic weights in a LIF neural network and learning rules. Physica D: Nonlinear Phenomena, 2017, 353-354, pp.20-30. ⟨10.1016/j.physd.2017.05.005⟩. ⟨hal-01541093⟩
712 Consultations
302 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More