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<p>Memristive neuron systems capable of generating multiscroll chaotic behavior offer a promising avenue for investigating neuronal dynamics and their broader applications. This paper attempts to couple a memristor with a 2-dimension (2D) neuron model to construct a new 3D hidden memristive chaotic HR neuron model (MHR), and further introduces a novel pulse function aimed at generating pulse-controlled hidden multiscroll attractors in the MHR (PMMHR). Notably, PMMHR enables flexible control of the number of multiscroll attractors via pulse parameter adjustment without altering the model structure, while simultaneously exhibiting large-scale symmetric offset boosting phenomena governed by memristor parameters. Hamiltonian energy analysis, Lyapunov exponents, phase portraits, bifurcation diagrams, and time series are employed to reveal the system’s dynamical properties. The feasibility of the PMMHR is experimentally validated utilizing a microcontroller unit (MCU) implementation. Finally, comprehensive statistical evaluations, including the NIST test, information entropy, byte histogram, key space analysis, and key sensitivity analysis, show that the pseudo-random number generator (PRNG) generated by the PMMHR possesses high randomness and good security performance.</p>

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Pulse-controlled hidden multiscroll attractors and large-scale symmetric offset boosting of a new 3D memristive neuron

  • 期刊论文

  • 发表日期：

    2026年6月14日

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  • Daxun Huang,
  • Qiang Lai,
  • Jianning Huang

摘要

Memristive neuron systems capable of generating multiscroll chaotic behavior offer a promising avenue for investigating neuronal dynamics and their broader applications. This paper attempts to couple a memristor with a 2-dimension (2D) neuron model to construct a new 3D hidden memristive chaotic HR neuron model (MHR), and further introduces a novel pulse function aimed at generating pulse-controlled hidden multiscroll attractors in the MHR (PMMHR). Notably, PMMHR enables flexible control of the number of multiscroll attractors via pulse parameter adjustment without altering the model structure, while simultaneously exhibiting large-scale symmetric offset boosting phenomena governed by memristor parameters. Hamiltonian energy analysis, Lyapunov exponents, phase portraits, bifurcation diagrams, and time series are employed to reveal the system’s dynamical properties. The feasibility of the PMMHR is experimentally validated utilizing a microcontroller unit (MCU) implementation. Finally, comprehensive statistical evaluations, including the NIST test, information entropy, byte histogram, key space analysis, and key sensitivity analysis, show that the pseudo-random number generator (PRNG) generated by the PMMHR possesses high randomness and good security performance.