Networks of integrate-and-fire neuron using rank order coding A: How to implement spike time dependent Hebbian plasticity

Abstract

Based on neurophysiological observations on the behavior of synapses, spike time dependent Hebbian plasticity is a novel extension to the modeling of the Hebb rule. This rule has enormous importance in the learning of spiking neural networks (SNN) but its mechanisms and computational properties are still to be explored. In this article, we present a generative model for spike time dependent plasticity based on a simplified model of the synaptic kinetic. We then explore the fitting of this model to experimental data and review some of its dynamical properties. Finally, we extend this model to a simplified model of integrate-and-fire (IF) neurons network using rank order coding.