A Heteroassociative Learning Model Robust to Interference

Randa Kassab; Frédéric Alexandre

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Paper

A Heteroassociative Learning Model Robust to Interference

Topics: Computational Neuroscience; Higher Level Artificial Neural Network Based Intelligent Systems; Learning Paradigms and Algorithms; Modular Implementation of Artificial Neural Networks; Supervised and Unsupervised Learning

In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: ECTA, 49-57, 2015 , Lisbon, Portugal

Authors: Randa Kassab and Frédéric Alexandre

Affiliation: Inria Bordeaux Sud-Ouest and Université de Bordeaux, France

Keyword(s): Associative Memory, Interference, Hippocampus.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computational Neuroscience ; Health Engineering and Technology Applications ; Higher Level Artificial Neural Network Based Intelligent Systems ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Modular Implementation of Artificial Neural Networks ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

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.

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Paper citation in several formats:

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

@conference{ncta15,
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 (ECTA 2015) - NCTA},
year={2015},
pages={49-57},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005606800490057},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence (ECTA 2015) - NCTA
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
PB - SciTePress