Spatiotemporal control of large neuronal networks using high dimensional optimization

项目摘要 该项目的长期目标是利用神经刺激来控制大脑中的大型网络 技术，这是BRAIN计划的一个重点。这些技术，包括光遗传学， 以前所未有的速度，因此，使科学家能够使越来越具体的外在 对神经回路活动的干扰。然而，这些扰动的性质在很大程度上仍然是 限制，使得受刺激的神经元群体被激活或失活。当科学家们试图 为了揭示大脑功能的更精细的机制，将需要一些方法， 时空活动模式-神经轨迹-在这些网络中诱导。巨大的规模 以及大脑中网络的相互联系使得这个问题变得非常重要。有人可能会把这比作 一个音乐家在舞台上试图引起一个特定的问题，从他们的每个成员独特的反应， 观众是一个人，而作为一个整体。为了更好地理解这些挑战， 为了超越它们，我们的建议引入了神经科学交叉点的早期概念， 控制理论，数学研究如何最佳地“引导”复杂系统受其动态， 可能的约束，以及一个目标函数，该目标函数测量期望的和诱导的约束之间的差异。 轨迹 我们的具体研究目标是基于我们团队在以下领域的跨学科经验： 动力系统、控制理论和神经科学。在目标1中，我们将研究架构和 大脑中网络的动力学使得能够控制自然输入，即，感觉兴奋 途径。换句话说，我们寻求深入了解大脑网络如何控制自己， 设计外在刺激。在目标2中，我们将开发一个新的工具包，从现代最优控制改编 工程，用于设计神经刺激输入波形，能够创建高维 轨迹（例如，尖峰的模式）在大的神经元网络中。为支持目标1和2，我们将制定一项 创新基准模型，包含许多突出神经元中普遍存在的结构和动力学特征 网络.最后，在目标3中，我们将进行体内实验，其中我们将部署我们的理论 在小鼠体感网络中诱导高维神经元轨迹的创新， 光遗传学 拟议的研究将以新的神经刺激设计的形式产生切实的成果 方法和基准控制模型，将传播到更广泛的神经科学 社区此外，我们的理论发展是对持续增长的重要补充。 刺激技术和细胞操作方法，促进更完整的方法来揭示 人类大脑的机制。

Project Summary The long terms goal of this project is to enable the control of large networks in the brain using neurostimulation technologies, a key focus of the BRAIN initiative. These technologies, including optogenetics, are developing at unprecedented rates and, consequently, are allowing scientists to make increasingly specific extrinsic perturbations to the activity in neural circuits. However, the nature of these perturbations remains largely limited so that the stimulated neuronal population is activated or deactivated en masse. As scientists seek to uncover the finer mechanisms of brain function, methods will be needed that allow more complex spatiotemporal activity patterns – neural trajectories – to be induced in these networks. The immense scale and interconnectedness of networks in the brain make this problem highly nontrivial. One may liken this problem to a musician on stage attempting to elicit a specific, unique response from each member of their audience individually, while playing to the group as a whole. To better understand these challenges and attempt to surpass them, our proposal introduces early concepts at the intersection of neuroscience and control theory, the mathematical study of how to optimally “steer” complex systems subject to their dynamics, possible constraints, and an objective function that measures differences between the desired and induced trajectories. Our specific research aims are grounded in our team's interdisciplinary experience at the interface of dynamical systems, control theory and neuroscience. In Aim 1, we will study how the architecture and dynamics of networks in the brain enable control with respect to natural inputs, i.e., excitation through sensory pathways. In other words, we seek insights into how brain networks control themselves, towards better designing extrinsic stimulation. In Aim 2, we will develop a new toolkit, adapted from modern optimal control engineering, for designing neurostimulation input waveforms that are capable of creating high-dimensional trajectories (e.g., patterns of spikes) in large neuronal networks. In support of Aims 1 and 2, we will develop an innovative benchmark model containing structural and dynamical features pervasive in many salient neuronal networks. Finally, in Aim 3, we will perform in vivo experiments in which we will deploy our theoretical innovations to induce high-dimensional neuronal trajectories in a mouse somatosensory network using optogenetics. The proposed research will yield tangible outcomes in the form of new neurostimulation design methodologies and a benchmark control model that will be disseminated to the broader neuroscience community. Further, our theoretical developments are an important complement to continued growth in stimulation technology and cellular manipulation methods, facilitating a more complete approach to uncovering the mechanisms of the human brain.