https://doi.org/10.1140/epjs/s11734-024-01297-x
Regular Article
Chaos, synchronization, and emergent behaviors in memristive hopfield networks: bi-neuron and regular topology analysis
1
Unité de Recherche d’Automatique et d’Informatique Appliquée (UR-AIA), IUT-FV Bandjoun University of Dschang, P.O. Box 134, Bandjoun, Cameroon
2
Southern Federal University, P.O. Box 344006, Rostov on Don, Russia
3
School of Digital Sciences, Digital University Kerala, Technocity campus, Mangalapuram, Kerala, India
4
Applied Physics Division, Center for Scientific Research and Higher Education at Ensenada, CICESE, Carr. Ensenada-Tijuana 3918, Zona Playitas, 22860, Ensenada, B. C., Mexico
Received:
24
April
2024
Accepted:
3
August
2024
Published online:
16
August
2024
This paper investigates the dynamics of a Hopfield inertial bi-neuron with double memristive synaptic weights. The dynamical behavior of the system is investigated with both numerical and analytical studies to characterize the proposed model, which has up to thirty-nine equilibrium points. In this model, numerical simulations show many behaviors such as chaos, antimonotonicity of periodic and chaotic bubbles, and bursting oscillation (regular and irregular). Moreover, this system showed multiple coexistence of up to six different attractors, with the attractor basins confirming this phenomenon. A ring and star network of Hopfield neurons was also considered. We found interesting spatio-temporal regimes, including chimera and cluster states. Moreover, we showed a striking coexistence of synchronized, chimera, and cluster states in the network. The integration of multiple memristors in neural network systems holds promise for improving our understanding of the brain and developing more sophisticated artificial intelligence technologies that can better mimic human cognitive abilities.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. 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.
