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Event-driven spiking neural networks with spike-based learning

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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.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China [Grant No. 2020AAA0105900]. The authors gave special thanks to Peisong Niu, Xi Wu and Runhao Jiang for their contributions to the experimental part of this paper. The authors would also like to thank the editors and the anonymous reviewers for their innovative suggestions, which improved the quality of this manuscript.

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, China

    Limiao Ning, Junfei Dong & Rong Xiao

  2. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Kay Chen Tan

  3. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Huajin Tang

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  1. Limiao Ning
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Correspondence to Rong Xiao.

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Ning, L., Dong, J., Xiao, R. et al. Event-driven spiking neural networks with spike-based learning. Memetic Comp. 15, 205–217 (2023). https://doi.org/10.1007/s12293-023-00391-2

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