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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 12

Fundamental numerical computational modeling based on adaptive loop attractor networks

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

Abstract

The article proposes a bio-inspired adaptive HDC network model, in which the operation law of mammalian brain head direction cells is mimicked, and the autonomous learning and error correction functions of the network are realized by using self-adjusting weights and Hebbian learning law. The experimental results found that, in terms of autonomous learning, the network weights can be adaptively adjusted based on the computational error, and the average convergence time is 2.5 iterations, which improves the learning efficiency by 60% compared with the fixed-weight method; in terms of error correction, the system is able to achieve an error correction rate of 94%, and the final error is stably controlled to within 3%. The ring topology model is formed with the help of LIF neurons, which realizes excitatory and inhibitory signal linkage state regulation ability, and after all kinds of neurological arithmetic tasks are processed through it, compared with the traditional fixed-weight method, the convergence time of adaptive learning is shortened by 60%, and the system shows an average of 35% reduction of error rate, whether it is 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. performance improvement of 35% reduction in error rate. 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 intelligence 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(2021YFF070420304)
中国科学院计算机网络与信息专项(2025000010)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.05.0166
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 12

Publish History

[2025-08-21] Accepted Paper

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). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.05.0166. (Chen Xinyu, Chen Zugang. Fundamental numerical computational modeling based on adaptive loop attractor networks [J]. Application Research of Computers, 2025, 42 (12). (2025-08-21). https://doi.org/10.19734/j.issn.1001-3695.2025.05.0166. )

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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