Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction

Understand

Spiking neural networks (SNNs) can be used in low-power and embedded systems (such as emerging neuromorphic chips) due to their event-based nature.

• Also, they have the advantage of low computation cost in contrast to conventional artificial neural networks (ANNs), while preserving ANN's properties.
• However, temporal coding in layers of convolutional spiking neural networks and other types of SNNs has yet to be studied.
• In this paper, we provide insight into spatio-temporal feature extraction of convolutional SNNs in experiments designed to exploit this property.