A Heteroassociative Learning Model Robust to Interference

Randa Kassab, Frédéric Alexandre


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.


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Paper Citation

in Harvard Style

Kassab R. and Alexandre F. (2015). A Heteroassociative Learning Model Robust to Interference . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 49-57. DOI: 10.5220/0005606800490057

in Bibtex Style

author={Randa Kassab and Frédéric Alexandre},
title={A Heteroassociative Learning Model Robust to Interference},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)},

in EndNote Style

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)
TI - A Heteroassociative Learning Model Robust to Interference
SN - 978-989-758-157-1
AU - Kassab R.
AU - Alexandre F.
PY - 2015
SP - 49
EP - 57
DO - 10.5220/0005606800490057