De Anandita, Kiani Roozbeh, Mazzucato Luca
Institute of Neuroscience, University of Oregon, Eugene, OR.
Center for Neural Science, New York University, New York, NY.
bioRxiv. 2025 Jun 4:2025.05.30.657050. doi: 10.1101/2025.05.30.657050.
Recent advancements in neurotechnology enable precise spatiotemporal patterns of microstimulations with single-cell resolution. The choice of perturbation sites must satisfy two key criteria: efficacy in evoking significant responses and selectivity for the desired target effects. This choice is currently based on laborious trial-and-error procedures, unfeasible for sequences of multi-site stimulations. Efficient methods to design complex perturbation patterns are urgently needed. Can we design a spatiotemporal pattern of stimulation to steer neural activity and behavior towards a desired target? We outline a method for achieving this goal in two steps. First, we identify the most effective perturbation sites, or hubs, only based on short observations of spontaneous neural activity. Second, we provide an efficient method to design multi-site stimulation patterns by combining approaches from nonlinear dynamical systems, control theory and data-driven methods. We demonstrate the feasibility of our approach using multi-site stimulation patterns in recurrent network models.
神经技术的最新进展使得能够以单细胞分辨率实现精确的微刺激时空模式。扰动位点的选择必须满足两个关键标准：引发显著反应的有效性以及对所需目标效应的选择性。目前，这种选择基于费力的试错程序，对于多位点刺激序列来说并不可行。迫切需要设计复杂扰动模式的有效方法。我们能否设计一种刺激的时空模式，将神经活动和行为导向期望的目标？我们概述了一种分两步实现这一目标的方法。首先，我们仅基于对自发神经活动的短期观察来确定最有效的扰动位点，即枢纽。其次，我们通过结合非线性动力系统、控制理论和数据驱动方法，提供一种设计多位点刺激模式的有效方法。我们在循环网络模型中使用多位点刺激模式证明了我们方法的可行性。
