https://doi.org/10.1140/epjp/s13360-025-07240-w
Regular Article
Analysis of associative memory models constructed based on memristor-coupled Hopfield neural networks
1
School of Information Science and Engineering, Dalian Polytechnic University, 116034, Dalian, China
2
School of Innovation and Entrepreneurship, Dalian Polytechnic University, 116034, Dalian, China
3
Communication Systems and Networks Research Group, Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
a
liuxiaod@dlpu.edu.cn
b
moujun@dlpu.edu.cn
Received:
25
October
2025
Accepted:
18
December
2025
Published online:
8
January
2026
Associative memory plays a crucial role in cognitive abilities, learning efficiency, and daily life. This paper proposes a new general-purpose memristor model and couples it with a Hopfield neural network (HNN) to construct a HNNM associative memory model. The stability of the HNNM system is analyzed. During the analysis of the parameters of the HNNM system, it is found that the system has rich dynamic characteristics, such as global hyperchaos and coexistence of attractors. By incorporating a multi-level step function into the system equations of HNNM, multi-cavity control of attractors is achieved. The sequence has a high degree of randomness, making it applicable to secure communications and industrial fields. Finally, the physical feasibility of the HNNM system is verified through the DSP platform. The HNNM model proposed in this paper provides a reference for research on associative memory in the field of brain-inspired research.
Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2026
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
