Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, Epiciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Conference papers

A Heteroassociative Learning Model Robust to Interference

Randa Kassab 1 Frédéric Alexandre 1, 2, 3 
1 Mnemosyne - Mnemonic Synergy
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, IMN - Institut des Maladies Neurodégénératives [Bordeaux]
Abstract : Neuronal models of associative memories are recurrent networks able to learn quickly patterns as stable states of the network. Their main acknowledged weakness is related to catastrophic interference when too many or too close examples are stored. Based on biological data we have recently proposed a model resistant to some kinds of interferences related to heteroassociative learning. In this paper we report numerical experiments that highlight this robustness and demonstrate very good performances of memorization. We also discuss convergence of interests for such an adaptive mechanism for biological modeling and information processing in the domain of machine learning.
Document type :
Conference papers
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download
Contributor : Frédéric Alexandre Connect in order to contact the contributor
Submitted on : Wednesday, November 25, 2015 - 10:26:11 PM
Last modification on : Saturday, June 25, 2022 - 7:42:21 PM


Files produced by the author(s)


  • HAL Id : hal-01232017, version 2



Randa Kassab, Frédéric Alexandre. A Heteroassociative Learning Model Robust to Interference. International Joint Conference on Computational Intelligence, Nov 2015, Lisboa, Portugal. ⟨hal-01232017⟩



Record views


Files downloads