  Spiking Neural Networks (SNN) are characterised by their unique temporal
dynamics, but the properties and advantages of such computations are still not
well understood. In order to provide answers, in this work we demonstrate how
Spiking neurons can enable temporal feature extraction in feed-forward neural
networks without the need for recurrent synapses, and how recurrent SNNs can
achieve comparable results to LSTM with a smaller number of parameters. This
shows how their bio-inspired computing principles can be successfully exploited
beyond energy efficiency gains and evidences their differences with respect to
conventional artificial neural networks. These results are obtained through a
new task, DVS-Gesture-Chain (DVS-GC), which allows, for the first time, to
evaluate the perception of temporal dependencies in a real event-based action
recognition dataset. Our study proves how the widely used DVS Gesture benchmark
can be solved by networks without temporal feature extraction when its events
are accumulated in frames, unlike the new DVS-GC which demands an understanding
of the order in which events happen. Furthermore, this setup allowed us to
reveal the role of the leakage rate in spiking neurons for temporal processing
tasks and demonstrated the benefits of "hard reset" mechanisms. Additionally,
we also show how time-dependent weights and normalization can lead to
understanding order by means of temporal attention.
