  One of the key challenges in training Spiking Neural Networks (SNNs) is that
target outputs typically come in the form of natural signals, such as labels
for classification or images for generative models, and need to be encoded into
spikes. This is done by handcrafting target spiking signals, which in turn
implicitly fixes the mechanisms used to decode spikes into natural signals,
e.g., rate decoding. The arbitrary choice of target signals and decoding rule
generally impairs the capacity of the SNN to encode and process information in
the timing of spikes. To address this problem, this work introduces a hybrid
variational autoencoder architecture, consisting of an encoding SNN and a
decoding Artificial Neural Network (ANN). The role of the decoding ANN is to
learn how to best convert the spiking signals output by the SNN into the target
natural signal. A novel end-to-end learning rule is introduced that optimizes a
directed information bottleneck training criterion via surrogate gradients. We
demonstrate the applicability of the technique in an experimental settings on
various tasks, including real-life datasets.
