Practical applications of spiking neural network in information processing and learning

Abstract

Historically, much of the research effort to contemplate the neural mechanisms involved in information processing in the brain has been spent with neuronal circuits and synaptic organization, basically neglecting the electrophysiological properties of the neurons. In this paper we present instances of a practical application using spiking neurons and temporal coding to process information, building a spiking neural network – SNN to perform a clustering task. The input is encoded by means of receptive fields. The delay and weight adaptation uses a multiple synapse approach. Dividing each synapse into sub-synapses, each one with a different fixed delay. The delay selection is then performed by a Hebbian reinforcement learning algorithm, also keeping resemblance with biological neural networks.