https://doi.org/10.1140/epjp/s13360-023-04183-y
Regular Article
Complex dynamics in a discrete adaptive synapse-based neuron model
School of Microelectronics and Control Engineering, Changzhou University, 213159, Changzhou, China
Received:
12
April
2023
Accepted:
11
June
2023
Published online:
19
June
2023
Discrete nonlinear systems usually have higher implementation efficiency than continuous nonlinear systems, but they can also exhibit dynamics diversity and chaos complex as continuous nonlinear systems. In this article, a two-dimensional (2D) discrete adaptive synapse-based neuron (DASN) model without external excitation is proposed using Euler’s discretization method. The proposed model has a complicated nonlinear activation function with upper and lower bounds, and its fixed points are not only variable in number, but also have different types of stability, resulting in the emergence of complex dynamics and multi-stability. Further, the dynamical effects of control parameters and initial values on the DASN model are explored using several numerical methods, and the complicated dynamical behaviors such as hyperchaos, chaos, quasi-period, period, stable point, quasi-periodic bifurcation, and period-doubling bifurcation are revealed thereby. Besides, a STM32-based hardware platform is exploited to digitally implement the DASN model and the unstable attractors are acquired experimentally to confirm the numerical ones.
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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.
