【报告摘要】Deep brain stimulation is clinically effective for movement disorders, yet the mechanisms by which high-frequency pulses reshape pathological synchronization remain poorly understood from a dynamical systems perspective. I present a vibrational control framework in which high-frequency periodic inputs effectively reshape a network's coupling structure, enabling selective stabilization of synchronization patterns. Using periodic averaging theory, I show how the stimulation waveform tunes the stability of an effective time-invariant system, and demonstrate that vibrational inputs can stabilize cluster synchronization—where distinct neural populations synchronize internally but maintain phase separation. I discuss implications for electrode placement, waveform design, and the path toward closed-loop, model-informed neuromodulation, including extensions to pulsatile protocols and personalized virtual brain models for epilepsy.

【报告人简介】秦语真博士，现任荷兰奈梅亨拉德堡德大学(Radboud University)机器学习与神经计算系助理教授。他于2012年和2015年分别在河海大学和武汉大学获得自动化专业学士和硕士学位，于2019年获得荷兰格罗宁根大学系统控制专业博士学位。2020至2023年间，他于美国加州大学河滨担任博士后研究员。他的主要研究方向包括复杂网络控制、非线性系统控制、强化学习，以及控制理论和机器学习在神经调控、闭环BCI中的应用。