An Improved Supervised Learning Algorithm Using Triplet-Based Spike-Timing-Dependent Plasticity

Abstract

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit arbitrary spike trains in response to given synaptic inputs. Recent years, the supervised learning algorithms based on synaptic plasticity have developed rapidly. As one of the most efficient supervised learning algorithms, the remote supervised method (ReSuMe) uses the conventional pair-based spike-timing-dependent plasticity rule, which depends on the precise timing of presynaptic and postsynaptic spikes. In this paper, using the triplet-based spike-timing-dependent plasticity, which is a powerful synaptic plasticity rule and acts beyond the classical rule, a novel supervised learning algorithm, dubbed T-ReSuMe, is presented to improve ReSuMe’s performance. The proposed algorithm is successfully applied to various spike trains learning tasks, in which the desired spike trains are encoded by Poisson process. The experimental results show that T-ReSuMe has higher learning accuracy and fewer iteration epoches than the traditional ReSuMe, so it is effective for solving complex spatio-temporal pattern learning problems.