  Current advances in technology have highlighted the importance of video
analysis in the domain of computer vision. However, video analysis has
considerably high computational costs with traditional artificial neural
networks (ANNs). Spiking neural networks (SNNs) are third generation
biologically plausible models that process the information in the form of
spikes. Unsupervised learning with SNNs using the spike timing dependent
plasticity (STDP) rule has the potential to overcome some bottlenecks of
regular artificial neural networks, but STDP-based SNNs are still immature and
their performance is far behind that of ANNs. In this work, we study the
performance of SNNs when challenged with the task of human action recognition,
because this task has many real-time applications in computer vision, such as
video surveillance. In this paper we introduce a multi-layered 3D convolutional
SNN model trained with unsupervised STDP. We compare the performance of this
model to those of a 2D STDP-based SNN when challenged with the KTH and Weizmann
datasets. We also compare single-layer and multi-layer versions of these models
in order to get an accurate assessment of their performance. We show that
STDP-based convolutional SNNs can learn motion patterns using 3D kernels, thus
enabling motion-based recognition from videos. Finally, we give evidence that
3D convolution is superior to 2D convolution with STDP-based SNNs, especially
when dealing with long video sequences.
