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Memristive effects on an improved discrete Rulkov neuron model

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Abstract

A change in neuronal-action potential can generate a magnetically induced current during the release and propagation of intracellular ions. To better characterize the electromagnetic-induction effect, this paper presents an improved discrete Rulkov (ID-Rulkov) neuron model by coupling a discrete model of a memristor with sine memductance into a discrete Rulkov neuron model. The ID-Rulkov neuron model possesses infinite invariant points, and its memristor-induced stability effect is evaluated by detecting the routes of period-doubling and Neimark-Sacker bifurcations. We investigated the memristor-induced dynamic effects on the neuron model using bifurcation plots and firing patterns. Meanwhile, we theoretically expounded the memristor initial-boosting mechanism of infinite coexisting patterns. The results show that the ID-Rulkov neuron model can realize diverse neuron firing patterns and produce hyperchaotic attractors that are nondestructively boosted by the initial value of the memristor, indicating that the introduced memristor greatly benefits the original neuron model. The hyperchaotic attractors initially boosted by the memristor were verified by hardware experiments based on a hardware platform. In addition, pseudorandom number generators are designed using the ID-Rulkov neuron model, and their high randomness is demonstrated based onstrict test results.

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Author information

Authors and Affiliations

  1. School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213159, China

    Han Bao, KeXin Li, Quan Xu & BoCheng Bao

  2. Department of Physics, Lanzhou University of Technology, Lanzhou, 730050, China

    Jun Ma

  3. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China

    ZhongYun Hua

Authors
  1. Han Bao
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  2. KeXin Li
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  3. Jun Ma
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  4. ZhongYun Hua
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  5. Quan Xu
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  6. BoCheng Bao
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Corresponding author

Correspondence to BoCheng Bao.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62271088 and 62201094) and the Scientific Research Foundation of Jiangsu Provincial Education Department, China (Grant No. 22KJB510001).

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Bao, H., Li, K., Ma, J. et al. Memristive effects on an improved discrete Rulkov neuron model. Sci. China Technol. Sci. 66, 3153–3163 (2023). https://doi.org/10.1007/s11431-023-2432-1

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  • DOI: https://doi.org/10.1007/s11431-023-2432-1

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