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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要该项目的长期目标是使用神经刺激来控制大脑中的大型网络技术，大脑计划的重点。这些技术，包括光遗传学，正在开发以前所未有的速度，因此允许科学家制作越来越具体的外部外部对神经回路活动的扰动。但是，这些扰动的性质在很大程度上仍然是有限，使刺激的神经元种群被激活或失活。作为科学家寻求发现脑功能的更精细的机制，需要进行更复杂的方法时空活动模式 - 神经轨迹 - 将在这些网络中诱导。巨大的规模大脑中网络的相互联系使这个问题高度不平凡。一个人可能喜欢这个舞台上的音乐家的问题，试图引起每个成员的特定，独特的回应观众单独参加整个小组。更好地了解这些挑战，并试图超越它们，我们的建议在神经科学和控制理论，即如何最佳“转向”复杂系统的数学研究，以其动态为准，可能的限制和一个目标函数，可以衡量所需和诱导之间的差异轨迹。我们的具体研究目的是基于团队的跨学科经验动态系统，控制理论和神经科学。在AIM 1中，我们将研究建筑和如何大脑中网络的动力学能够控制自然输入，即通过感觉兴奋途径。换句话说，我们寻求有关大脑网络如何控制自己的见解设计外部刺激。在AIM 2中，我们将开发一个新的工具包，该工具包适用于现代最佳控制工程，用于设计能够创建高维的神经刺激输入波形大型神经元网络中的轨迹（例如，尖峰模式）。为了支持目标1和2，我们将开发一个在许多显着神经元中包含结构和动态特征的创新基准模型网络。最后，在AIM 3中，我们将进行体内实验，我们将在其中部署我们的理论使用鼠标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形式产生切实的结果方法和基准控制模型将被传播到更广泛的神经科学社区。此外，我们的理论发展是持续增长的重要完成刺激技术和细胞操纵方法，支持一种更完整的方法人脑的机制。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
作者：
Liu,Sensen;Sock,NoahM;Ching,ShiNung
通讯作者：
Ching,ShiNung