The Spike Gating Flow: A Hierarchical Structure Based Spiking Neural Network for Spatiotemporal Computing

Current deep learning faces major challenges for action recognition tasks because of: 1) the huge computational cost and 2) the inefficient learn- ing. Hence, we develop a novel Spiking Neural Network (SNN) titled Spiking Gating Flow (SGF) for such a dilemma. 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 train- ing layer. By employing a dynamic visions sensor gesture dataset, the results indicate that we can achieve 87.5% accuracy which is comparable with Deep Learning (DL), but at smaller train- ing/inference data number ratio 1.5:1. And only a single training epoch is required during the learn- ing process. 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 con- tent based global dynamic feature detection.