Skip to Main content Skip to Navigation
Journal articles

Distributed synaptic weights in a LIF neural network and learning rules

Abstract : 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.
Document type :
Journal articles
Complete list of metadatas

Cited literature [25 references]  Display  Hide  Download

https://hal.sorbonne-universite.fr/hal-01541093
Contributor : Benoît Perthame <>
Submitted on : Saturday, June 17, 2017 - 12:23:13 PM
Last modification on : Friday, March 27, 2020 - 3:33:24 AM
Document(s) archivé(s) le : Friday, December 15, 2017 - 3:48:32 PM

Files

distrib_connec_HAL.pdf
Files produced by the author(s)

Identifiers

Citation

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

Share

Metrics

Record views

889

Files downloads

747