An Extensive Review of the Supervised Learning Algorithms for Spiking Neural Networks

A Spiking Neural Network (SNN) processes neural information through precise timing of spikes and is considered a brain-inspired computational model of the third generation of the artificial neural network. SNN has a set of biologically plausible spiking neurons that have proven effective in processing complex temporal and spatio-temporal data. In addition, SNNs are computationally powerful, energy-efficient as well as a dynamic systems. However, the formulation of efficient supervised learning algorithms for SNNs is challenging due to their inherently discontinuous and implicit non-linear mechanisms. It has become a significant challenge in this field. Moreover, there exist a few efficient supervised learning algorithms developed for SNN. This paper provides a thorough review of supervised learning algorithms developed for SNNs categorically. We have divided the supervised learning algorithms into several categories based on the core principles for optimisation, such as gradient rule, asymmetric supervised Hebbian learning, remote supervision, and metaheuristics.