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Research Article Open access CC BY 4.0

A Survey of Supervised Learning Models for Spiking Neural Network

Moses Apambila Agebure, Paula Aninyie Wumnaya, Edward Yellakuor Baagyere

Asian Journal of Research in Computer Science · pp. 35–49 · Published 17 Jun 2021

10.9734/ajrcos/2021/v9i430228

Abstract

There has been a significant attempt to derive supervised learning models for training Spiking Neural Networks (SNN), which is the third and most recent generation of Artificial Neural Network (ANN). Supervised SNN learning models are considered more biologically plausible and thus exploits better the computational efficiency of biological neurons and also, are less computationally expensive than second generation ANN. SNN models have also produced competitive performance in most tasks when compared to second generation ANNs. These advantages, coupled with the difficulty in adopting the well established learning models for second generation networks to train SNN due to the difference in information coding led to the recent introduction of supervised learning models for training SNN. However, lack of comprehensive source of literature detailing strides made in this area, and the challenges and prospects of SNN serves as a hindrance to further exploration and application of SNN models. A comprehensive review of supervised learning methods in SNN is presented in this paper in which some widely used SNN neural models, learning models and their basic concepts, areas of applications, limitations, prospects and future research directions are discussed. The main contribution of this paper is that it presents and discusses trends in supervised learning in SNN with the aim of providing a reference point for those desiring further knowledge and application of SNN methods.

Spiking neural network supervised learning classification spike sequence learning artificial neural network

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