Computing with spiking neuron networks a review

Abstract

Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike firing. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Today, the main challenge is to discover efficient learning rules that might take advantage of the specific features of SNNs while keeping the nice properties (general-purpose, easy-to-use, available simulators, etc.) of traditional connectionist models. This paper presents the history of the "spiking neuron", summarizes the most currently-in-use models of neurons and synaptic plasticity, the computational power of SNNs is addressed and the problem of learning in networks of spiking neurons is tackled