Neuromorphic behaviors in a neuron circuit based on current-controlled Chua Corsage Memristor

IF 5.6 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Zhijun Li , Kaijie Chen
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Abstract

Locally active memristor is considered as an ideal device for building neuron circuits. In this study, a novel current-controlled Chua Corsage Memristor (CCM), which supplements the existing CCM family, is proposed to explore its unknown neuromorphic dynamics. The non-volatility of the current-controlled CCM is verified by its power-off plot and the locally active domain is identified by its DC I-V plot. A third-order neuron circuit is developed by embedding the current-controlled CCM into a passive LC network. The edge of chaos domain of the neuron circuit is identified only by real parts of the eigenvalues of the system's Jacobi matrix. The resulting circuit is capable of sensing external current stimuli and producing neuromorphic behaviors when it is poised on or near the edge of chaos, making it more bionic. The neuromorphic behaviors in four different parameter intervals are revealed in detail, and the generation mechanisms are analyzed based on the theories of Hopf bifurcation and edge of chaos. Due to its simple structure and complex neuromorphic behaviors, it is expected that the neuron circuit may help to further study memristor-based neuron models.

基于电流控制Chua-Corsage忆阻器的神经元电路中的神经形态行为
局部有源忆阻器被认为是构建神经元电路的理想器件。在本研究中,提出了一种新的电流控制Chua-Corsage忆阻器(CCM),它补充了现有的CCM家族,以探索其未知的神经形态动力学。电流控制CCM的非挥发性通过其断电图得到验证,局部活性域通过其DC I-V图得到识别。通过将电流控制的CCM嵌入无源LC网络,开发了一种三阶神经元电路。神经元电路的混沌域的边缘仅由系统的雅可比矩阵的特征值的实部来识别。由此产生的电路能够感应外部电流刺激,并在处于混沌边缘或接近混沌边缘时产生神经形态行为,使其更加仿生。详细揭示了四个不同参数区间的神经形态行为,并基于Hopf分岔和混沌边缘理论分析了其产生机制。由于其简单的结构和复杂的神经形态行为,预计神经元电路可能有助于进一步研究基于忆阻器的神经元模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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