  The growing interest in brain-inspired computational models arises from the
remarkable problem-solving efficiency of the human brain. Action recognition, a
complex task in computational neuroscience, has received significant attention
due to both its intricate nature and the brain's exceptional performance in
this area. Nevertheless, current solutions for action recognition either
exhibit limitations in effectively addressing the problem or lack the necessary
biological plausibility. Deep neural networks, for instance, demonstrate
acceptable performance but deviate from biological evidence, thereby
restricting their suitability for brain-inspired computational studies. On the
other hand, the majority of brain-inspired models proposed for action
recognition exhibit significantly lower effectiveness compared to deep models
and fail to achieve human-level performance. This deficiency can be attributed
to their disregard for the underlying mechanisms of the brain. In this article,
we present an effective brain-inspired computational model for action
recognition. We equip our model with novel biologically plausible mechanisms
for spiking neural networks that are crucial for learning spatio-temporal
patterns. The key idea behind these new mechanisms is to bridge the gap between
the brain's capabilities and action recognition tasks by integrating key
biological principles into our computational framework. Furthermore, we
evaluate the performance of our model against other models using a benchmark
dataset for action recognition, DVS-128 Gesture. The results show that our
model outperforms previous biologically plausible models and competes with deep
supervised models.
