https://doi.org/10.1140/epjp/s13360-023-04772-x
Regular Article
A memristor-coupled heterogeneous discrete neural networks with infinite multi-structure hyperchaotic attractors
1
School of Information Science and Engineering, Dalian polytechnic University, 116034, Dalian, China
2
School of Food Science and Technology, State Key Laboratory of Marine Food Processing and Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic University, 116034, Dalian, China
3
Institute of Electrical and Electronics Engineers, M2J 4A6, Toronto, Canada
Received:
5
October
2023
Accepted:
3
December
2023
Published online:
19
December
2023
In this paper, a discrete memristor-coupled heterogeneous neural network (MCHNN) by coupling KTz neuron and tabu learning neurons with a locally active discrete memristor is constructed. Firstly, rationale of the proposed discrete MCHNN is presented. Then, taking the memristor initial state and coupling weight as variables, the abundant dynamical behaviors of the coupled neuron network are systematically analyzed, as well as the multi-stability of the discrete bi-neuron network are proved. Particularly, the six different firing cases and three types infinite multi-structure hyperchaotic attractors are discussed. Finally, a microcontroller-based hardware experiment was also conducted, in order to further verify the correctness of the numerical simulation. This study provides a theoretical basis for the implementation of MCHNN in human brain dynamics.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. 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.
