Tutorial: Neuromorphic spiking neural networks for temporal learning

Abstract

Spiking neural networks (SNNs), as time-dependent hypotheses consisting of spiking nodes (neurons) and directed edges (synapses), are believed to offer unique solutions to reward prediction tasks and the related feedback that are classified as reinforcement learning. Generally, temporal difference (TD) learning renders it possible to optimize a model network to predict the delayed reward in an ad hoc manner. Neuromorphic SNNs—networks built using dedicated hardware—particularly leverage such TD learning for not only reward prediction but also temporal sequence prediction in a physical time domain. In this tutorial, such learning in a physical time domain is referred to as temporal learning to distinguish it from conventional TD learning-based methods that generally involve algorithmic (rather than physical) time. This tutorial addresses neuromorphic SNNs for temporal learning from scratch. It first concerns the general characteristics of SNNs including spiking neurons and information coding schemes and then moves on to temporal learning including its general concept, feasible algorithms, and its association with neurophysiological learning rules that have been intensively enriched in the last few decades.