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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 2
593-597

Self-adaptive coding for spiking neural network

Zhang Chi1,2,3
Tang Fengzhen1,2
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China
2. Institutes for Robotics & Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
DOI: 10.19734/j.issn.1001-3695.2021.06.0239

Abstract

Using spikes to represent and convey information, SNN is more biologically plausible than traditional artificial neural networks. However, a classical SNN has limited feature extraction ability due to the shallow network structure, leading to inferior classification performance to CNN especially on multi-class classification tasks such as object categorization. Inspired by the powerful convolutional structure of CNN, this paper proposed a SCSNN. By exploiting convolutional structures and dynamic impulse triggered property of biological neurons, the proposed SCSNN organized integrate-and-fire models in a convolutional fashion, and trained by a new surrogate gradient back-propagation algorithm directly. It validated the proposed SCSNN on the MNIST and Fashion-MNIST dataset respectively, obtaining superior performance to state-or-the-art SNN on both datasets. The classification accuracy reaches 99.62% on the MNIST dataset, and 93.52% on the Fashion-MNIST dataset, which verifies the effectiveness of the proposed model.

Foundation Support

国家重点研发计划资助项目(2020YFB13400)
国家自然科学基金资助项目(61803369)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.06.0239
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 2
Section: Technology of Graphic & Image
Pages: 593-597
Serial Number: 1001-3695(2022)02-047-0593-05

Publish History

[2021-08-31] Accepted Paper
[2022-02-05] Printed Article

Cite This Article

张驰, 唐凤珍. 基于自适应编码的脉冲神经网络 [J]. 计算机应用研究, 2022, 39 (2): 593-597. (Zhang Chi, Tang Fengzhen. Self-adaptive coding for spiking neural network [J]. Application Research of Computers, 2022, 39 (2): 593-597. )

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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