Tutorial: Neuromorphic spiking neural networks for temporal learning

TL;DR

This tutorial explores neuromorphic spiking neural networks for temporal learning, emphasizing physical time domain processing, and discusses their characteristics, algorithms, and neurophysiological foundations.

Contribution

It provides a comprehensive introduction to neuromorphic SNNs for temporal learning, highlighting their unique physical time domain capabilities and related neurophysiological learning rules.

Findings

Neuromorphic SNNs leverage physical time for temporal learning.

Various algorithms and neurophysiological rules are associated with SNNs.

SNNs offer potential advantages in reward and sequence prediction.

Abstract

Spiking neural networks (SNN) 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 the scratch. It first concerns 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 their association with neurophysiological learning rules that have intensively been enriched for the last few decades.