  Video analysis is a computer vision task that is useful for many applications
like surveillance, human-machine interaction, and autonomous vehicles. Deep
Convolutional Neural Networks (CNNs) are currently the state-of-the-art methods
for video analysis. However they have high computational costs, and need a
large amount of labeled data for training. In this paper, we use Convolutional
Spiking Neural Networks (CSNNs) trained with the unsupervised Spike
Timing-Dependent Plasticity (STDP) learning rule for action classification.
These networks represent the information using asynchronous low-energy spikes.
This allows the network to be more energy efficient and neuromorphic
hardware-friendly. However, the behaviour of CSNNs is not studied enough with
spatio-temporal computer vision models. Therefore, we explore transposing
two-stream neural networks into the spiking domain. Implementing this model
with unsupervised STDP-based CSNNs allows us to further study the performance
of these networks with video analysis. In this work, we show that two-stream
CSNNs can successfully extract spatio-temporal information from videos despite
using limited training data, and that the spiking spatial and temporal streams
are complementary. We also show that using a spatio-temporal stream within a
spiking STDP-based two-stream architecture leads to information redundancy and
does not improve the performance.
