Objective. The rapid acceleration of tools for recording neuronal populations and targeted optogenetic manipulation has enabled real-time, feedback control of neuronal circuits in the brain. Continuously-graded control of measured neuronal activity poses a wide range of technical challenges, which we address through a combination of optogenetic stimulation and a state-space optimal control framework implemented in the thalamocortical circuit of the awake mouse. Approach. Closed-loop optogenetic control of neurons was performed in real-time via stimulation of channelrhodopsin-2 expressed in the somatosensory thalamus of the head-fixed mouse. A state-space linear dynamical system model structure was used to approximate the light-to-spiking input-output relationship in both single-neuron as well as multi-neuron scenarios when recording from multielectrode arrays. These models were utilized to design state feedback controller gains by way of linear quadratic optimal control and were also used online for estimation of state feedback, where a parameter-adaptive Kalman filter provided robustness to model-mismatch. Main results. This model-based control scheme proved effective for feedback control of single-neuron firing rate in the thalamus of awake animals. Notably, the graded optical actuation utilized here did not synchronize simultaneously recorded neurons, but heterogeneity across the neuronal population resulted in a varied response to stimulation. Simulated multi-output feedback control provided better control of a heterogeneous population and demonstrated how the approach generalizes beyond single-neuron applications. Significance. To our knowledge, this work represents the first experimental application of state space model-based feedback control for optogenetic stimulation. In combination with linear quadratic optimal control, the approaches laid out and tested here should generalize to future problems involving the control of highly complex neural circuits. More generally, feedback control of neuronal circuits opens the door to adaptively interacting with the dynamics underlying sensory, motor, and cognitive signaling, enabling a deeper understanding of circuit function and ultimately the control of function in the face of injury or disease.

客观的。用于记录神经元群体和有针对性的光遗传学操作的工具的快速加速已经实现了对大脑神经元回路的实时反馈控制。对测量的神经元活动的连续分级控制带来了广泛的技术挑战，我们通过结合光遗传学刺激和在清醒小鼠的丘脑皮质电路中实施的状态空间最佳控制框架来解决这些挑战。方法。通过刺激在头部固定小鼠的体感丘脑中表达的通道视紫红质-2，实时进行神经元的闭环光遗传学控制。当从多电极阵列记录时，使用状态空间线性动态系统模型结构来近似单神经元和多神经元场景中的光-尖峰输入-输出关系。这些模型用于通过线性二次最优控制设计状态反馈控制器增益，也用于在线估计状态反馈，其中参数自适应卡尔曼滤波器为模型失配提供鲁棒性。主要结果。这种基于模型的控制方案证明对清醒动物丘脑中单神经元放电率的反馈控制是有效的。值得注意的是，这里使用的分级光学驱动没有同步同时记录的神经元，但神经元群体的异质性导致对刺激的不同反应。模拟多输出反馈控制提供了对异质群体的更好控制，并展示了该方法如何推广到单神经元应用之外。意义。据我们所知，这项工作代表了基于状态空间模型的反馈控制在光遗传学刺激中的首次实验应用。结合线性二次最优控制，这里列出和测试的方法应该推广到未来涉及高度复杂神经回路控制的问题。更一般地说，神经元回路的反馈控制为自适应地与感觉、运动和认知信号传导的动力学相互作用打开了大门，从而能够更深入地了解回路功能，并最终在面对损伤或疾病时控制功能。