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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Data distribution statistics and batch normalization based on membrane potential increments of spiking neurons

Li Weiqi1
Chen Yunhua1
Chen Pinghua1
Zhu Chunjia2
1. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China
2. China Shipbuilding Research & Design Center, Neijiang Sichuan 641199, China
DOI: 10.19734/j.issn.1001-3695.2024.12.0529

Abstract

Spiking Neural Networks (SNNs) have garnered significant attention due to their low power consumption and high-speed computation, stemming from their avoidance of multiplication operations. However, substantial challenges remain in the areas of training algorithms, hyperparameter tuning, and architecture design for SNNs. Addressing the limitations of existing Batch Normalization (BN) methods in effectively handling temporal dependencies, this paper analyzes the propagation of membrane potential increments across time steps. The proposed method computes the spatio-temporal accumulation of membrane potential increments step-by-step as statistical measures for normalizing data at each time step. Furthermore, an exponentially weighted moving average is introduced to compute the spatio-temporal accumulation of membrane potential increments, forming a Spatio-Temporal Attenuation Cumulative Batch Normalization (STBN) method. Experimental results on the CIFAR-10, CIFAR-100, and CIFAR10-DVS datasets demonstrate that the proposed method significantly improves network classification accuracy and reduces latency. Notably, on the CIFAR-100 dataset, the method achieved an accuracy of 76.30% using only two time steps, representing a 3.43% improvement over the previous best algorithm for similar models.

Foundation Support

广东省自然科学基金资助项目(2025A1515012243)
广东省重点领域研发计划资助项目(2023B1111050010)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.12.0529
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-04-16] Accepted Paper

Cite This Article

李炜奇, 陈云华, 陈平华, 等. 基于脉冲神经元膜电位增量的数据分布统计量及批归一化 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0529. (Li Weiqi, Chen Yunhua, Chen Pinghua, et al. Data distribution statistics and batch normalization based on membrane potential increments of spiking neurons [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2024.12.0529. )

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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