A progressive surrogate gradient learning for memristive spiking neural network

doi:10.1088/1674-1056/acb9f6

Abstract In recent years, spiking neural networks (SNNs) have received increasing attention of research in the field of artificial intelligence due to their high biological plausibility, low energy consumption, and abundant spatio-temporal information. However, the non-differential spike activity makes SNNs more difficult to train in supervised training. Most existing methods focusing on introducing an approximated derivative to replace it, while they are often based on static surrogate functions. In this paper, we propose a progressive surrogate gradient learning for backpropagation of SNNs, which is able to approximate the step function gradually and to reduce information loss. Furthermore, memristor cross arrays are used for speeding up calculation and reducing system energy consumption for their hardware advantage. The proposed algorithm is evaluated on both static and neuromorphic datasets using fully connected and convolutional network architecture, and the experimental results indicate that our approach has a high performance compared with previous research.

Keywords: spiking neural network surrogate gradient supervised learning memristor cross array

Received: 13 November 2022
Revised: 07 January 2023
Accepted manuscript online: 08 February 2023

PACS:	87.19.L-	(Neuroscience)
	87.19.ll	(Models of single neurons and networks)
	89.20.Ff	(Computer science and technology)

Fund: Project supported by the Natural Science Foundation of Chongqing (Grant No. cstc2021jcyj-msxmX0565), the Fundamental Research Funds for the Central Universities (Grant No. SWU021002), and the Graduate Research Innovation Project of Chongqing (Grant No. CYS22242).

Corresponding Authors: Shu-Kai Duan
E-mail: duansk@swu.edu.cn

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Shu Wang(王姝), Tao Chen(陈涛), Yu Gong(龚钰), Fan Sun(孙帆), Si-Yuan Shen(申思远), Shu-Kai Duan(段书凯), and Li-Dan Wang(王丽丹) A progressive surrogate gradient learning for memristive spiking neural network 2023 Chin. Phys. B 32 068704

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A progressive surrogate gradient learning for memristive spiking neural network

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