  Action recognition is an exciting research avenue for artificial intelligence
since it may be a game changer in the emerging industrial fields such as
robotic visions and automobiles. However, current deep learning faces major
challenges for such applications because of the huge computational cost and the
inefficient learning. Hence, we develop a novel brain-inspired Spiking Neural
Network (SNN) based system titled Spiking Gating Flow (SGF) for online action
learning. The developed system consists of multiple SGF units which assembled
in a hierarchical manner. A single SGF unit involves three layers: a feature
extraction layer, an event-driven layer and a histogram-based training layer.
To demonstrate the developed system capabilities, we employ a standard Dynamic
Vision Sensor (DVS) gesture classification as a benchmark. The results indicate
that we can achieve 87.5% accuracy which is comparable with Deep Learning (DL),
but at smaller training/inference data number ratio 1.5:1. And only a single
training epoch is required during the learning process. Meanwhile, to the best
of our knowledge, this is the highest accuracy among the non-backpropagation
algorithm based SNNs. At last, we conclude the few-shot learning paradigm of
the developed network: 1) a hierarchical structure-based network design
involves human prior knowledge; 2) SNNs for content based global dynamic
feature detection.
