Wan Zhiqiang, Pu Yi-Fei, Lai Qiang
College of Computer Science, Sichuan University, Chengdu 610065, China.
School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China.
Neural Netw. 2025 Aug;188:107473. doi: 10.1016/j.neunet.2025.107473. Epub 2025 Apr 17.
In current neurodynamic studies, memristor models using polynomial or multiple nested composite functions are primarily employed to generate multiscroll attractors, but their complex mathematical form restricts both research and application. To address this issue, without relying on polynomial and multiple nested composite functions, this study devises a unique memristor model and a memristive autapse HR (MAHR) neuron model featuring multiscroll hidden attractor. Specially, the quantity of scrolls within the multiscroll hidden attractors is regulated by simulation time. Besides, a simple control factor is incorporated into the memristor to improve the MAHR neuron model. Numerical analysis further finds that the quantity of scrolls within the multiscroll hidden attractor from the improved MAHR neuron model can be conveniently adjusted by only changing a single parameter or initial condition of the memristor. Moreover, a microcontroller-based hardware experiment is conducted to confirm that the improved MAHR neuron model is physically feasible. Finally, an elegant image encryption scheme is proposed to explore the real-world applicability of the improved MAHR neuron model.
在当前的神经动力学研究中，主要采用使用多项式或多个嵌套复合函数的忆阻器模型来生成多涡卷吸引子，但其复杂的数学形式限制了研究和应用。为了解决这个问题，本研究在不依赖多项式和多个嵌套复合函数的情况下，设计了一种独特的忆阻器模型和一个具有多涡卷隐藏吸引子的忆阻自突触HR（MAHR）神经元模型。特别地，多涡卷隐藏吸引子内的涡卷数量由仿真时间调节。此外，在忆阻器中引入一个简单的控制因子以改进MAHR神经元模型。数值分析进一步发现，通过仅改变忆阻器的单个参数或初始条件，就可以方便地调整改进后的MAHR神经元模型的多涡卷隐藏吸引子内的涡卷数量。此外，进行了基于微控制器的硬件实验，以确认改进后的MAHR神经元模型在物理上是可行的。最后，提出了一种精妙的图像加密方案，以探索改进后的MAHR神经元模型在现实世界中的适用性。
