Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction

Spiking neural networks (SNNs) can be used in low-power and embedded systems e.g. neuromorphic chips due to their event-based nature. They preserve conventional artificial neural networks (ANNs) properties with lower computation and memory costs. The temporal coding in layers of convolutional SNNs has not yet been studied. In this paper, we exploit the spatio-temporal feature extraction property of convolutional SNNs. Based on our analysis, we have shown that the shallow convolutional SNN outperforms spatio-temporal feature extractor methods such as C3D, ConvLstm, and cascaded Conv and LSTM. Furthermore, we present a new deep spiking architecture to tackle real-world classification and activity recognition tasks. This model is trained with our proposed hybrid training method. The proposed architecture achieved superior performance compared to other SNN methods on NMNIST (99.6%), DVS-CIFAR10 (69.2%), and DVS-Gesture (96.7%). Also, it achieves comparable results compared to ANN methods on UCF-101 (42.1%) and HMDB-51 (21.5%) datasets.