Hebbian Learning In A Multimodal Environment
Julien Hubert, Eiko Matsuda, Takashi Ikegami · 2013
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.