https://doi.org/10.1140/epjs/s11734-026-02156-7
Regular Article
A magnetron memristor coupled with a heterogeneous cell neural network: design, analysis and circuit implementation
Jinan Key Laboratory of Memristive Computing and Applications (JKLMCA), Qilu Institute of Technology, 250200, Jinnan, China
a
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Received:
1
August
2025
Accepted:
22
January
2026
Published online:
12
February
2026
Abstract
In order to address the bottlenecks of traditional memristor-based Cellular Neural Networks (CNNs) in neuromorphic computing, such as the lack of practical electromagnetic scenarios in coupling mechanisms and the ambiguous definition of heterogeneity, this study proposes a novel magnetron memristor-coupled heterogeneous Cellular Neural Network (CNN) model. Magnetron memristors are employed for the first time as synaptic coupling elements and a leakage flux term (− εφ) is introduced to simulate energy loss in real electromagnetic environments. This constructs an electromagnetic coupling mechanism consistent with practical scenarios. Meanwhile, subnet heterogeneity is explicitly defined as a difference in output control weights (α1 ≠ α2), enabling the impact of key parameters on coupled dynamics can be analysed in isolation under the premise of consistent subnet structures. Numerical simulations and PSpice circuit experiments reveal diverse multistable behaviours, including periodic limit cycles, single-scroll chaos, double-scroll chaotic attractor and hyperchaos, as well as unique dynamic phenomena such as alternating period-doubling bifurcation, reverse periodic evolution and the symmetric coexistence of double-scroll chaotic attractor. The dynamic characteristics observed in experiments and simulations are highly consistent, verifying the physical realizability of the proposed model and providing a new perspective for the hardware design of neuromorphic computing.
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© The Author(s), under exclusive licence to EDP Sciences, 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.
