System Development & Application
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 7
2134-2140

Deep spiking neural network training method across spiking propagation

Zeng Jianxin
Chen Yunhua
Li Weiqi
Chen Pinghua
School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0562

Abstract

Backpropagation-based training methods for SNNs still face many problems and challenges, including that the spike firing process is non-differentiable and spike neurons have complex spatiotemporal dynamics processes. In addition, SNNs backpropagation training methods often do not consider the relationship of the error signal between adjacent spikes, greatly reducing the accuracy of the model. To this end, this paper proposed a cross-spike error backpropagation training method for deep spiking neural networks(CSBP), which divided the error backpropagation of neurons into two dependencies: the dependency of spike firing time with the postsynaptic membrane potential(DSFT) and the dependency between spike firing time(DBSFT). Among them, DSFT solved the problem of spike non-differentiability and DBSFT clarified the dependence between spikes, allowing error signals to propagate across spikes, improving biological rationality. In addition, this paper solved the problem of insufficient expressive ability in early spiking ResNet network architecture by modifying the structural order of the spike residual block. Experimental results show that the proposed method is significantly improved compared to the SOTA(state-of-the-art) training algorithms based on spike time. Under the same architecture, the improvement is 2.98% on the CIFAR10 dataset, and 2.26% on the DVS-CIFAR10 dataset.

Foundation Support

国家社会科学基金资助项目(20BKG031)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0562
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 7
Section: System Development & Application
Pages: 2134-2140
Serial Number: 1001-3695(2024)07-029-2134-07

Publish History

[2024-02-01] Accepted Paper
[2024-07-05] Printed Article

Cite This Article

曾建新, 陈云华, 李炜奇, 等. 跨脉冲传播的深度脉冲神经网络训练方法 [J]. 计算机应用研究, 2024, 41 (7): 2134-2140. (Zeng Jianxin, Chen Yunhua, Li Weiqi, et al. Deep spiking neural network training method across spiking propagation [J]. Application Research of Computers, 2024, 41 (7): 2134-2140. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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