Event-driven spiking neural networks with spike-based learning

Limiao Ning, Junfei Dong, Rong Xiao, Kay Chen Tan, Huajin Tang

Abstract: Spiking neural networks (SNNs) use spikes to communicate between neurons, leading to biological plausible implementation. Considering spikes as events, SNNs are inherently suitable for processing address event representation (AER) data. Despite the progress in event-driven methods for AER data, there is little study on the relationship between time-driven and event-driven algorithms, that is required to gain insight into the understanding of SNNs. In this paper, an in-depth analysis of time-driven and event-driven algorithms was given. A same-timestamp problem in event-driven simulation, which may lead to an error spike, is found and solved in a simple efficacious way. An event-driven learning algorithm was proposed, which is efficient and compatible with a multitude of spike-based plasticity mechanisms. Leaky integrate-and-fire neurons with precise spike driven synaptic plasticity was used to demonstrate the property of the proposed event-driven algorithm and conduct experiments on two AER datasets (MNIST-DVS and AER Poker Card) and MNIST dataset. The results show that the event-driven simulation is always faster than the time-driven simulation, and the proposed algorithm achieves similar accuracy to other conventional time-driven methods.