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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 1
177-182,187

Improved adaptive threshold algorithm for double threshold spiking neural network

Wang Haojie
Liu Chuang
School of Information Engineering, Shenyang University, Shenyang 110044, China
DOI: 10.19734/j.issn.1001-3695.2023.05.0210

Abstract

SNN has gained widespread attention due to its low power consumption and high-speed computing capabilities on neuromorphic chips. The conversion from DNN to SNN is an effective training method for SNN. However, there are approximation errors in the conversion process, leading to significant performance degradation of the converted SNN under short time steps. Through a detailed analysis of the errors in the conversion process, this paper decomposed them into quantization and pruning errors and asymmetric errors, and proposed an improved adaptive threshold algorithm to balance the threshold of SNN. It used the mean square error(MMSE) to achieve a better balance between quantization and pruning errors. Additionally, this algorithm introduced a dual-threshold memory mechanism based on the IF neuron model to effectively address the asymmetric errors. Experimental results demonstrate that the improved algorithm achieves excellent performance on the CIFAR-10, CIFAR-100 datasets, and the MIT-BIH arrhythmia dataset. For the CIFAR-10 dataset, it achieves a high accuracy of 93.22% with only 16 time steps, validating the effectiveness of the algorithm.

Foundation Support

辽宁省自然科学基金资助项目(2023-MS-322)
中国博士后科学基金会资助项目(2021M693858)
沈阳市中青年科技创新人才支持计划资助项目(RC210400)
辽宁省自然科学基金计划重点项目(20170520364)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0210
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 1
Section: Algorithm Research & Explore
Pages: 177-182,187
Serial Number: 1001-3695(2024)01-026-0177-06

Publish History

[2023-09-13] Accepted Paper
[2024-01-05] Printed Article

Cite This Article

王浩杰, 刘闯. 用于双阈值脉冲神经网络的改进自适应阈值算法 [J]. 计算机应用研究, 2024, 41 (1): 177-182,187. (Wang Haojie, Liu Chuang. Improved adaptive threshold algorithm for double threshold spiking neural network [J]. Application Research of Computers, 2024, 41 (1): 177-182,187. )

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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