  Spiking neural networks (SNNs) can be used in low-power and embedded systems
(such as emerging neuromorphic chips) due to their event-based nature. Also,
they have the advantage of low computation cost in contrast to conventional
artificial neural networks (ANNs), while preserving ANN's properties. However,
temporal coding in layers of convolutional spiking neural networks and other
types of SNNs has yet to be studied. In this paper, we provide insight into
spatio-temporal feature extraction of convolutional SNNs in experiments
designed to exploit this property. The shallow convolutional SNN outperforms
state-of-the-art spatio-temporal feature extractor methods such as C3D,
ConvLstm, and similar networks. Furthermore, we present a new deep spiking
architecture to tackle real-world problems (in particular classification tasks)
which achieved superior performance compared to other SNN methods on NMNIST
(99.6%), DVS-CIFAR10 (69.2%) and DVS-Gesture (96.7%) and ANN methods on UCF-101
(42.1%) and HMDB-51 (21.5%) datasets. It is also worth noting that the training
process is implemented based on variation of spatio-temporal backpropagation
explained in the paper.
