Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 12
3628-3636

Fundamental numerical computational modeling based on adaptive loop attractor network

Chen Xinyu1
Chen Zugang2
1. School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
DOI: 10.19734/j.issn.1001-3695.2025.05.0166

Abstract

This article proposed a bio-inspired adaptive HDC network model. It mimicked the operation law of mammalian brain head direction cells, and realized the autonomous learning and error correction functions of the network by using self-adjusting weights and Hebbian learning law. The experimental results found that in terms of autonomous learning, the network weights could be adaptively adjusted based on the computational error, and the average convergence time was 2.5 iterations, which improved the learning efficiency by 60% compared with the fixed-weight method. In terms of error correction, the system was able to achieve an error correction rate of 94%, and the final error was stably controlled to within 3%. The ring topo-logy model was formed with the help of LIF neurons, which realized excitatory and inhibitory signal linkage state regulation ability, and after all kinds of neurological arithmetic tasks were processed through it. Compared with the traditional fixed-weight method, the convergence time of adaptive learning was shortened by 60%, and the system showed an average of 35% reduction of error rate, whether it was in the processing of results beyond the range of the overflow of the ［0, 99］, or in the processing of the different spans of the additive and subtractive arithmetic tasks. This study provides a new research idea for the development of bio-inspired intelligent computation, which is an important reference for the advancement of artificial intelli-gence induced by brain science and has the possibility of popularization and application.

Foundation Support

国家自然科学基金资助项目(42201505)
海南省自然科学基金资助项目(622QN352)
国家重点研发计划资助项目(2021YFF070420304)
中国科学院计算机网络与信息专项资助项目(2025000010)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.05.0166
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 12
Section: Algorithm Research & Explore
Pages: 3628-3636
Serial Number: 1001-3695(2025)12-013-3628-09

Publish History

[2025-08-21] Accepted Paper
[2025-12-05] Printed Article

Cite This Article

陈信宇, 陈祖刚. 基于自适应环吸引子网络的基础数值计算模型 [J]. 计算机应用研究, 2025, 42 (12): 3628-3636. (Chen Xinyu, Chen Zugang. Fundamental numerical computational modeling based on adaptive loop attractor network [J]. Application Research of Computers, 2025, 42 (12): 3628-3636. )

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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