  Inspired by the operation of biological brains, Spiking Neural Networks
(SNNs) have the unique ability to detect information encoded in spatio-temporal
patterns of spiking signals. Examples of data types requiring spatio-temporal
processing include logs of time stamps, e.g., of tweets, and outputs of neural
prostheses and neuromorphic sensors. In this paper, the second of a series of
three review papers on SNNs, we first review models and training algorithms for
the dominant approach that considers SNNs as a Recurrent Neural Network (RNN)
and adapt learning rules based on backpropagation through time to the
requirements of SNNs. In order to tackle the non-differentiability of the
spiking mechanism, state-of-the-art solutions use surrogate gradients that
approximate the threshold activation function with a differentiable function.
Then, we describe an alternative approach that relies on probabilistic models
for spiking neurons, allowing the derivation of local learning rules via
stochastic estimates of the gradient. Finally, experiments are provided for
neuromorphic data sets, yielding insights on accuracy and convergence under
different SNN models.
