Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon calcium imaging enables the measurement of ongoing and induced activity across the neural population. Despite the enormous space of potential photostimulation patterns and the time-consuming nature of photostimulation experiments, very little algorithmic work has been done to determine the most effective photostimulation patterns for identifying the neural population dynamics. Here, we develop methods to efficiently select which neurons to stimulate such that the resulting neural responses will best inform a dynamical model of the neural population activity. Using neural population responses to photostimulation in mouse motor cortex, we demonstrate the efficacy of a low-rank linear dynamical systems model, and develop an active learning procedure which takes advantage of low-rank structure to determine informative photostimulation patterns. We demonstrate our approach on both real and synthetic data, obtaining in some cases as much as a two-fold reduction in the amount of data required to reach a given predictive power. Our active stimulation design method is based on a novel active learning procedure for low-rank regression, which may be of independent interest.

翻译：近年来，监测与调控神经群体的技术进展极大地增强了我们研究大脑环路的能力。特别是，双光子全息光遗传学现能实现对实验者指定的单个神经元群体进行精确光刺激，而同步的双光子钙成像则可测量神经群体中持续存在及被诱发的活动。尽管潜在的光刺激模式空间极为庞大，且光刺激实验本身耗时费力，但关于如何确定最有效识别神经群体动力学的光刺激模式的算法研究却极为有限。本文开发了高效选择刺激神经元的方法，使得由此产生的神经响应能够最有效地揭示神经群体活动的动力学模型。利用小鼠运动皮层对光刺激的神经群体响应，我们验证了低秩线性动力学系统模型的有效性，并开发了一种主动学习流程，该流程利用低秩结构来确定信息量丰富的光刺激模式。我们在真实数据与合成数据上验证了该方法，在某些情况下实现了达到给定预测能力所需数据量减少多达两倍的效果。我们的主动刺激设计方法基于一种新颖的低秩回归主动学习流程，该方法可能具有独立的学术价值。