Spatiotemporal control of large neuronal networks using high dimensional optimization

使用高维优化对大型神经元网络进行时空控制

• 批准号: 9356504
• 负责人: ShiNung Ching
• 金额: $ 23.82万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9356504
• 关键词: Adverse effects; Architecture; BRAIN initiative; Beds; Behavior; Benchmarking; Brain; Brain region; Clinic; Clinical; Code; Communities; Complement; Complex; Computer software; Deep Brain Stimulation; Development; Engineering; Future; Goals; Growth; Human; Individual; Lighting; Link; Mathematics; Measurement; Measures; Methodology; Methods; Modeling; Modernization; Motivation; Mus; Nature; Neurons; Neurosciences; Noise; Outcome; Output; Parkinson Disease; Pathway interactions; Pattern; Physiologic pulse; Play; Population; Property; Research; Rodent; Scientist; Sensory; Source; Stereotyping; Structure; Structure-Activity Relationship; System; Systems Analysis; Techniques; Technology; Testing; Time; Uncertainty; Vibrissae; blind; brain cell; brain machine interface; cell type; computational neuroscience; control theory; design; dynamic system; electrical microstimulation; engineering design; experience; experimental study; high dimensionality; improved; in vivo; innovation; insight; instrumentation; member; musician; nervous system disorder; neural circuit; neural model; neural patterning; neural prosthesis; neural stimulation; neurophysiology; neuroregulation; open source; optogenetics; response; somatosensory; spatiotemporal; theories; time use

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.

项目摘要 这个项目的长期目标是通过神经刺激来控制大脑中的大型网络。 技术，这是大脑倡议的一个关键重点。这些技术，包括光遗传学，正在发展中 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 神经回路中活动的扰动。然而，这些扰动的性质在很大程度上仍然存在 限制，以使受刺激的神经元群体被整体激活或去激活。当科学家们试图 揭示大脑功能的更精细机制，将需要允许更复杂的方法 时空活动模式--神经轨迹--在这些网络中被诱导。巨大的规模 大脑中网络的互联性使这个问题变得非常重要。有人可能会把这比作 舞台上的音乐家试图从他们的每个成员那里获得特定的、独特的反应 单独的观众，同时向整个团队演奏。为了更好地了解这些挑战和 为了试图超越它们，我们的提案在神经科学和 控制论，关于如何根据复杂系统的动力学来最佳地“操纵”系统的数学研究， 可能的约束，以及衡量期望和诱导之间的差异的目标函数 轨迹。 我们的具体研究目标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，我们将研究体系结构和 大脑中的网络动态使人能够控制自然输入，即通过感觉兴奋 小路。换句话说，我们寻求对大脑网络如何控制自己的洞察，以实现更好的 设计外在刺激。在目标2中，我们将开发一个新的工具包，以适应现代最优控制 工程学，用于设计能够创建高维的神经刺激输入波形 在大型神经元网络中的轨迹(例如，尖峰模式)。为支持目标1和目标2，我们将制定一个 包含许多显著神经元中普遍存在的结构和动力学特征的创新基准模型 网络。最后，在目标3中，我们将进行活体实验，在其中我们将应用我们的理论 在小鼠躯体感觉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结果 方法论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 刺激技术和细胞操纵方法，有助于更完整地揭示 人脑的机制。

项目成果

Fundamental Limits of Forced Asynchronous Spiking with Integrate and Fire Dynamics.

使用 Integrate 和 Fire Dynamics 强制异步尖峰的基本限制。

• DOI: 10.1186/s13408-017-0053-5
• 发表时间: 2017
• 期刊: Journal of mathematical neuroscience
• 影响因子: 2.3
• 作者: Nandi,Anirban;Schättler,Heinz;Ritt,JasonT;Ching,ShiNung
• 通讯作者: Ching,ShiNung

Learning-based Approaches for Controlling Neural Spiking.

基于学习的控制神经尖峰的方法。

• DOI: 10.23919/acc.2018.8431158
• 发表时间: 2018
• 期刊: Proceedings of the ... American Control Conference. American Control Conference
• 作者: Liu,Sensen;Sock,NoahM;Ching,ShiNung
• 通讯作者: Ching,ShiNung