https://doi.org/10.1140/epjs/s11734-025-01617-9
Regular Article
Dynamic behaviors and digital circuit implementation of a Rulkov neuron with a non-polynomial memristor synaptic weight
1
Department of Telecommunication and Network Engineering, IUT-Fotso Victor of Bandjoun, University of Dschang, P. O. Box: 134, Bandjoun, Cameroon
2
Research Unit of Automation and Applied Computer, Department of Electrical Engineering, IUT-FV Bandjoun, University of Dschang, P. O. Box: 134, Bandjoun, Cameroon
3
Faculty of Electronics Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam
4
Department of Engineering for Innovation, University of Salento, 73100, Lecce, Italy
Received:
5
February
2025
Accepted:
31
March
2025
Published online:
12
April
2025
Memristors have proven since their discovery to play a very important role as artificial synapses in the implementation of artificial neural networks. Just as biological synapses, memristors can significantly influence the behaviors of the neural network. A non-polynomial memristor is used in this work to investigate the memristive synaptic weight effect on the dynamics of a simplified Rulkov neuron. The model is first introduced from where theoretical analyses show the system’s ability to develop regular and irregular behaviors. Analysis methods such as two parameter charts, bifurcation diagrams and maximum Lyapunov exponents (MLE) are then used to study the dynamics of the proposed Rulkov model. The numerical results show that the memristive synaptic weight Rulkov neuron model can exhibit self-excited and hidden firings. Furthermore, the system can undergo interesting offset boosting dynamics and antimonotonicity features (i.e., bubble bifurcations) for some system parameters. To corroborate the computational findings, the proposed model is implemented on an Arduino Due microcontroller board, from where the experimental results are seen to be in agreement with the obtained numerical results.
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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.
