Bifurcation Analysis and Energy Modulation of ML Neurons Under Noise
JIANG Lingfeng1, AN Xinlei2, LI Hongwei1, HE Weijun1, LENG Zhenbei1
1. School of Mathematics and Computer Science, Chongqing College of International Business and Economics, Chongqing 401520, China; 2. School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China
Abstract:To investigate the effect of noise on the discharge patterns of neurons and the synchronization stability between neurons, this paper considers the Morris-Lecar (ML) neuron model and explores its dynamical behavior by means of single-two-parameter bifurcation diagrams, Lyapunov exponent diagrams, and phase diagrams. The results show that the system not only exists rich bifurcation behaviors but also is sensitive to the initial value and exhibits multi-stable coexistence states. Next, the energy balance and synchronization patterns of the coupled system before and after the addition of noise are compared by coupling the ML neurons through a magnetic flux-controlled memristor. It is found that the coupled system can achieve energy balance and complete synchronization regardless of whether noise is added. These results provide new guidelines for identifying synaptic adaptability.
蒋玲凤, 安新磊, 李宏伟, 何伟军, 冷震北. 噪声作用下ML神经元的分岔分析和能量调制[J]. 复杂系统与复杂性科学, 2026, 23(3): 104-111.
JIANG Lingfeng, AN Xinlei, LI Hongwei, HE Weijun, LENG Zhenbei. Bifurcation Analysis and Energy Modulation of ML Neurons Under Noise[J]. Complex Systems and Complexity Science, 2026, 23(3): 104-111.
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