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Spiking neural networks (SNNs) have attracted attention as the third generation of neural networks for their promising characteristics of energy-efficiency and biological plausibility. The diversity of spiking neuron models and architectures have made various learning algorithms developed. This paper provides a gentle survey of SNNs to give an overview of what they are and how they are trained. It first presents how biological neurons works and how they are mathematically modelled specially in differential equations. Next it categorizes the learning algorithms of SNNs into groups and presents how their representative algorithms work. Then it briefly describe the neuromorphic hardware on which SNNs run.

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