  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.
