忆阻器 ML 神经元的动态行为及其在安全通信中的应用

IF 1.6 4区 物理与天体物理 Q3 PHYSICS, CONDENSED MATTER
Kaijun Wu, Zhaoxue Huang, Mingjun Yan
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引用次数: 0

摘要

在真实的生理环境中改进神经元并研究其电行为,对于了解人类大脑认知功能和神经动力学至关重要。神经元细胞处于复杂的生理环境中,离子跨膜运动产生的电磁场会影响其放电活动。因此,为了更好地模拟生物神经元的真实情况,本文结合忆阻器的特性,在三维 ML 神经元模型中加入磁控忆阻器,构建了四维 Morris-Lecar (ML)神经元模型。通过研究时间序列图、相平面图、尖峰间期(ISI)分叉图,我们探索了反馈增益系数和膜电位与磁通量之间的关系系数对模型中神经元发射行为的影响。结果发现,这两个参数的变化会导致神经元复杂的点火模式。我们还利用最大李雅普诺夫指数和耗散理论研究了基于忆阻器的 ML 神经元模型中的混沌同步现象。此外,我们还探讨了系统内噪声对神经元同步行为的影响,发现适当的噪声强度能有效加速神经元达到同步状态。最后,我们将混沌同步系统应用于安全通信,仿真结果和相关分析表明,该系统在加密和解密语音信号方面表现出色,具有很高的安全性和保密性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Dynamic behavior of memristor ML neurons and its application in secure communication

Dynamic behavior of memristor ML neurons and its application in secure communication

Dynamic behavior of memristor ML neurons and its application in secure communication

Improving neurons in a real physiological environment and studying their electrical behavior is crucial for understanding human cognitive brain functions and neural dynamics. Neuronal cells reside in a complex physiological environment, where the electromagnetic fields generated by ion transmembrane movements affect their discharge activity. Therefore, to better simulate the real conditions of biological neurons, this paper incorporated the characteristics of the memristor and constructed a four-dimensional Morris-Lecar (ML) neuron model by adding a magneto-controlled memristor into the three-dimensional ML neuron model. Through the study of time series diagrams, phase plane diagrams, inter-spike interval (ISI) bifurcation diagrams, we explored the effects of the feedback gain coefficient and the relationship coefficient between membrane potential and magnetic flux on the firing behavior of neurons in the model. It was found that variations in these two parameters can lead to complex firing patterns in neurons. We also utilized the maximum Lyapunov exponent and dissipative theory to investigate the chaotic synchronization phenomenon in the memristor-based ML neuron model. Additionally, we explored the impact of noise on neuronal synchronization behavior within the system, finding that an appropriate noise intensity can effectively accelerate the neurons’ attainment of a synchronized state. Finally, applying the chaotic synchronization system to secure Communication, the simulation results and related analysis demonstrate that the system excels in encrypting and decrypting voice signals, offering high levels of security and confidentiality.

Graphical abstract

Simulation results of speech signal encryption and decryption

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来源期刊
The European Physical Journal B
The European Physical Journal B 物理-物理:凝聚态物理
CiteScore
2.80
自引率
6.20%
发文量
184
审稿时长
5.1 months
期刊介绍: Solid State and Materials; Mesoscopic and Nanoscale Systems; Computational Methods; Statistical and Nonlinear Physics
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