https://doi.org/10.1140/epjs/s11734-025-01669-x
Regular Article
Pinning control of a multistable memristive neuronal network
College of Mechanical and Electronic Engineering, Yantai Institute of Science and Technology, 265600, Yantai, China
Received:
12
March
2025
Accepted:
26
April
2025
Published online:
14
May
2025
Synchronization and controllability in multistable neuronal networks play a vital role in understanding brain dynamics, with implications for information processing, memory formation, and neurological disorder treatment. This paper presents a novel investigation into the synchronization and controllability of a multistable memristive neuronal network composed of locally active memristive Hindmarsh-Rose neurons. The model exhibits coexisting chaotic and periodic bursting dynamics, dependent on initial conditions. Using master stability function analysis and synchronization error metrics, we demonstrate that chaotic synchronization requires a minimum coupling strength, while periodic synchronization occurs effortlessly across all coupling strengths. A distinctive aspect of this study is the implementation of pinning control to steer the network from chaotic dynamics to target states: a periodic manifold and an unstable fixed point (resting state). We introduce a comparative analysis of three node-selection strategies—sequential, uniformly distributed, and random. Results reveal that uniformly distributed pinning outperforms other strategies, requiring lower feedback gains for control. Stabilizing the network to the unstable fixed point demands significantly higher feedback gain and more pinned nodes compared to periodic synchronization. The interplay between coupling strength and feedback gain is critical, with stronger coupling reducing the required control effort. These findings contribute new insights into the design of efficient control strategies for multistable neuronal networks, highlighting the role of topology, node selection, and parameter tuning in achieving desired dynamical states.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
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.
