混合振荡神经元网络中反相干共振向相干共振的转迁  

TRANSITION FROM ANTI-COHERENCE RESONANCE TO COHERENCE RESONANCE IN A NETWORK COMPOSED OF NEURONS WITH MIXED-MODE OSCILLATIONS

作　　者：吴媛梦 贾雁兵 Wu Yuanmeng;Jia Yanbing(School of Mathematics and Statistics,Henan University of Science and Technology,Luoyang,471000,China)

机构地区：[1]河南科技大学数学与统计学院,洛阳471000

出　　处：《动力学与控制学报》2022年第5期76-86,共11页Journal of Dynamics and Control

基　　金：国家自然科学基金资助项目(11802086);河南省科技攻关项目(202102310410)资助课题。

摘　　要：混合振荡和相干共振广泛存在于生物神经系统,且与某些生理功能有密切联系.本文采用能产生混合振荡的改进FitzHugh–Nagumo神经元模型构建电耦合神经元网络模型.在确定性的网络模型中,发现耦合强度的增大不仅能使得神经元的放电达到完全同步,还能使得放电模式从混合振荡变为周期1峰放电.引入高斯白噪声后,发现当耦合强度在较大范围内时,随着噪声强度的增大,放电峰峰间期的变差系数先增大后减小再增大,即出现反相干共振向相干共振的转迁.该结果不仅扩展了神经元网络的复杂随机动力学,还揭示了混合振荡的潜在功能.Mixed-mode oscillations and coherence resonance widely exist in the biological nervous system and are closely related to certain physiological functions.In this paper,a improved FitzHugh-Nagumo neuronal model that can generate mixed-mode oscillations is used to construct an electrically coupled neuronal network model.In the deterministic network model,it is shown that through increasing the coupling strength,the neuronal firing reaches complete synchronization and the mixed-mode oscillation turns into period-1 spiking.After introducing Gaussian white noise to the network,it is shown that,when the coupling strength is within a large range,the coefficient variation of interspike intervals first increases,then decreases,and further increases if increasing the noise intensity,implying that transition from anti-coherence resonance to coherence resonance occurs.The results not only expands complex stochastic dynamics of the neuronal networks,but also reveals potential functions of mixed-mode oscillations.

关 键 词：混合振荡 相干共振 反相干共振 神经元网络 

分 类 号：O324[理学—一般力学与力学基础]