An online supervised learning algorithm based on triple spikes for spiking neural networks

TL;DR

This paper introduces an online supervised learning algorithm for spiking neural networks that uses triple spikes for direct synaptic regulation, improving accuracy and efficiency without restricting neuron types.

Contribution

It proposes a novel online learning method based on triple spikes and spatial-temporal transformation, overcoming limitations of existing algorithms.

Findings

Higher learning accuracy than existing algorithms

Improved learning efficiency

Applicable to various neuron models

Abstract

Using precise times of every spike, spiking supervised learning has more effects on complex spatial-temporal pattern than supervised learning only through neuronal firing rates. The purpose of spiking supervised learning after spatial-temporal encoding is to emit desired spike trains with precise times. Existing algorithms of spiking supervised learning have excellent performances, but mechanisms of them still have some problems, such as the limitation of neuronal types and complex computation. Based on an online regulative mechanism of biological synapses, this paper proposes an online supervised learning algorithm of multiple spike trains for spiking neural networks. The proposed algorithm with a spatial-temporal transformation can make a simple direct regulation of synaptic weights as soon as firing time of an output spike is obtained. Besides, it is also not restricted by types of spiking neuron models. Relationship among desired output, actual output and input spike trains is firstly analyzed and synthesized to simply select a unit of pair-spike for a direct regulation. And then a computational method is constructed based on simple triple spikes using this direct regulation. Compared with other learning algorithms, results of experiments show that proposed algorithm has higher learning accuracy and efficiency.