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DOI: http://dx.doi.org/10.7551/978-0-262-31709-2-ch100
Pages 698-705
First published 2 September 2013

Hebbian Learning In A Multimodal Environment

Julien Hubert, Eiko Matsuda, Takashi Ikegami

Abstract

Hebbian learning is a classical non-supervised learning algorithm used in neural networks. Its particularity is to transcribe the correlations between couple of neurons within their connecting synapse. From this idea, we created a robotic task where 2 sensory modalities indicate the same target in order to find out if a neural network equipped with Hebbian learning could naturally exploit the relation between those modalities. Another question we explored is the difference in terms of learning between a feedforward neural network (FNN) and spiking neural network (SNN). Our results indicate that a FNN can partially exploit the relation between the modalities and the task when receiving a feedback from a teacher. We also found out that a SNN could not complete the task because of the nature of the Hebbian learning modeled.