https://doi.org/10.1140/epjp/s13360-025-06111-8
Regular Article
A bistable locally active memristor multisynaptically coupled to Rulkov neurons
School of Information Science and Engineering, Dalian Polytechnic University, 116034, Dalian, Ganjingzi District, China
a
workingfine@163.com
b
moujun@dlpu.edu.cn
Received:
5
September
2024
Accepted:
10
February
2025
Published online:
7
March
2025
The study of memristor simulation of neuronal synapses has been more extensive and in-depth. However, the study of simulation of neuronal connectivity structure in the cerebral cortex has not yet attracted people's attention. In this paper, a novel bistable locally active discrete memristor is proposed as a neuronal autosynapse and synapse to simulate the connection structure of neurons in the cerebral cortex. Dynamical methods such as equilibrium point stability, Lyapunov exponential spectrum and bifurcation diagrams are utilized for analytical studies. Numerical simulations reveal that the proposed multisynaptic coupled Rulkov neural network has multiple brain-like firing patterns. Multiple attractor phase diagrams with periodic-periodic, periodic-chaotic, and chaotic-chaotic coexistence as well as high complexity are found. Digital signal processing-based hardware implementation platform was also developed, on which the attractor phase diagrams realized by the simulation platform were experimentally captured. New ideas are provided for the future construction of cerebral cortical neuron models.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and 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.
