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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 7

Online supervised learning algorithm for synaptic delays of spiking neurons with multiple synaptic connections

Wang Xiangwen1,2
Zou Li3
Fan Jingxing1
1. College of Information Engineering, Gansu Minzu Normal University, Hezuo Gansu 747000, China
2. College of Computer Science & Engineering, Northwest Normal University, Lanzhou Gansu 730070, China
3. Gansu Provincial Meteorological Information & Technical Support & Equipment Center, Lanzhou Gansu 730020, China
DOI: 10.19734/j.issn.1001-3695.2024.11.0496

Abstract

Neuroscience studies have shown that synaptic delay plays a positive role in neural information processing, and multiple synaptic connections are widely distributed in the nervous system. However, most of the current spiking neural networks are modeled with a single synaptic connection mode, and the influence of synaptic delay is not fully considered in the design of supervised learning algorithms, which limits their potential performance. This paper constructed a network of spiking neurons with multiple synaptic connections, and proposed a biologically plausible online supervised learning algorithm to simultaneously optimize the synaptic weights and synaptic delays of spiking neurons. The algorithm constructed a real-time error function using the kernel function representation of spike trains, and derived real-time update rules for synaptic weights and synaptic delays using the gradient descent method. The results of spike train learning and nonlinear pattern classification tasks show that the dynamic synaptic delay learning algorithm has higher learning accuracy and requires fewer learning epochs than the static synaptic delay learning algorithm, and the learning accuracy of multiple synaptic connections is higher than that of single synaptic connections. It can be seen that the synaptic delay plasticity and multiple synaptic connection mode can effectively improve the learning performance of spiking neural networks.

Foundation Support

甘肃省自然科学基金项目(24JRRA127)、甘肃省青年科技基金项目(23JRRA1577)、西北师范大学青年教师科研能力提升计划项目(NWNU-LKQN2024-21)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0496
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 7

Publish History

[2025-03-14] Accepted Paper

Cite This Article

王向文, 邹丽, 范景行. 多突触连接脉冲神经元的突触延迟在线监督学习算法 [J]. 计算机应用研究, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0496. (Wang Xiangwen, Zou Li, Fan Jingxing. Online supervised learning algorithm for synaptic delays of spiking neurons with multiple synaptic connections [J]. Application Research of Computers, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0496. )

About the Journal

  • 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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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