Enriched dynamical behavior of a novel locally active memristor-driven neuron map

Tao Ma, Jun Mou and Wanzhong Chen

Chaos, Solitons & Fractals, 2025, vol. 198, issue C

Abstract: The construction of neuron models using memristors with bionic properties can provide new ideas for brain-like research. This paper proposes a novel discrete locally active memristor (DLAM) designed to drive neuron map to generate complex chaotic dynamics. The nonvolatility and locally active properties of the proposed memristor are exhaustively investigated. The bifurcation behavior is analyzed by varying the DLAM-dependent parameters and interesting Feigenbaum remerging trees are found. Moreover, the variation of the memristor parameters is capable of triggering multistability and generating complex heterogeneous coexistence. Adjusting the initial conditions of the memristor was able to induce offset-boosted coexistence with a hybrid topology. Finally, a pseudo random sequence generator (PRNG) is designed using chaotic sequences generated by DLAM-driven neuron map and shows excellent performance. A DSP experimental platform was built for numerical simulation verification. The novel DLAM is proposed to provide new insights for the study of nonlinear behavior in neuron models.

Keywords: Memristor; Feigenbaum remerging trees; Multistability; DSP implementation; PRNG (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:198:y:2025:i:c:s0960077925005508

DOI: 10.1016/j.chaos.2025.116537

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