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Interaction of external inputs with internal dynamics: influence of brain states on neural computation and behavior

外部输入与内部动态的相互作用：大脑状态对神经计算和行为的影响

基本信息

批准号：
10687134
负责人：
Karl A. Deisseroth
金额：
$ 382.03万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-17 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10687134
关键词：
3-Dimensional Acceleration Astronomy BRAIN initiative Back Bayesian Analysis Behavior Behavioral Brain Brain region Cells Central Nervous System Chemistry Clinical Cognition Collaborations Communities Computer Models Computing Methodologies Data Diagnosis Dimensions Education and Outreach Ensure Environment Esthesia Functional disorder Hydrogels Image Individual Injury Language Laws Learning Macaca Maintenance Measurement Measures Medial Methods Modeling Molecular Monitor Monkeys Motor Mus Neurons Neurosciences Optics Outcome Pattern Perception Phenotype Play Population Positioning Attribute Preparation Process Psyche structure Research Rodent Science Sensory Stream Synapses System Technology Testing Text Time Tissues Uncertainty Update Visual Visual Cortex Work Writing analog cell assembly cell cortex cell type clinically relevant computational neuroscience computational platform computerized tools control theory design empowerment experience experimental study feeding frontal eye fields insight laboratory experiment meetings millisecond multimodality multisensory neural neural circuit neural model neuronal circuitry neuropsychiatric disorder new technology next generation nonhuman primate outreach sensory input single cell sequencing social spatiotemporal success technology development technology platform temporal measurement theories two-photon

项目摘要

Overall - Interaction of external inputs with internal dynamics: influence of brain states on neural computation and behavior Project Summary A central challenge in neuroscience involves understanding how assemblies of cortical neurons, comprised of different cell types and inhabiting different layers, work together to generate coherent dynamical internal states, that then interact with external sensory inputs to generate state-dependent behaviors on a moment-by-moment basis. Key impediments to meeting this foundational challenge include lack of adequate technological and computational tools to monitor, control, identify and model neural state dynamics emerging from cortical cell assemblies spanning multiple cortical cell-types and layers. We propose to develop an unprecedented confluence of technology and computation to achieve such capabilities by building on our team’s significant prior work. In particular, our combined technology and computation platform will enable us to: (1) perform volumetric imaging of thousands of cortical cells during behavior to collect both relevant spatiotemporal activity patterns and 3D positioning; (2) simultaneously write arbitrary spatiotemporal patterns into tens to hundreds of individually identified cells at millisecond temporal resolution using 2-photon multiSLM methods; and (3) using hydrogel tissue-chemistry and single-cell sequencing methods, obtain deep molecular cell-type information in the same neurons that were both measured and controlled during behavior. This unprecedented simultaneous read/write/cell-typing technology will be tightly integrated with computational methods that can: (1) employ state of the art systems identification methods to identify and extract neural states and the dynamical laws governing their interactions with external inputs; and (2) amongst the astronomical number of possible spatiotemporal stimulation patterns, predict interesting ones that can best refine models, yield conceptual insights, and yield the capacity for optimal control of cortical circuit dynamics, with potential clinical relevance. This combined technology and computation will empower next-generation experiments that allow us to learn the dynamical language (in terms of state space dynamics) of cortical circuits, play back modified versions of this language for both insight and control, and understand how this language emerges from the concerted activity of multiple cell-types across layers. Our technology/computation platform will be validated in multiple experiments across species and brain regions, guided by deep and long-standing theories of internal state dynamics in computational neuroscience. Throughout, new methods will be collaboratively validated in the diverse preparations of our experimental labs (such cross-cutting interactions are shown in blue text). In particular we will focus on testing theories underlying several foundational classes of neural computation: (1) ability of sensory networks to generate accurate percepts by detecting and amplifying weak sensory inputs amidst spontaneous background activity; (2) Bayesian integration of multisensory inputs to convert sensorimotor experiences into internal estimates of external state variables and their uncertainty; and (3) triggering and maintenance of discrete internal attractor states capable of controlling stable behavior.

总体-外部投入与内部动态的相互作用：大脑状态对神经计算和行为的影响项目摘要神经科学的一个核心挑战是理解皮层神经元的组装，不同的细胞类型和居住在不同的层，一起工作以产生相干的动态内部状态，然后与外部感官输入相互作用，以产生时刻依赖于状态的行为，基础应对这一基本挑战的主要障碍包括缺乏足够的技术和用于监视、控制、识别和模拟从皮层细胞出现的神经状态动力学的计算工具组装跨越多个皮质细胞类型和层。我们提议建立一个前所未有的技术和计算来实现这样的能力，通过建立在我们的团队的重要先前的工作。在特别是，我们的组合技术和计算平台将使我们能够：（1）执行体积成像数以千计的皮层细胞在行为过程中收集相关的时空活动模式和3D 定位;（2）同时将任意时空模式写入数十到数百个单独的使用2-光子多SLM方法以毫秒时间分辨率鉴定细胞;和（3）使用水凝胶组织化学和单细胞测序方法，获得深层分子细胞类型信息，在行为过程中被测量和控制的神经元。这种前所未有的同时读/写/单元打字技术将与计算方法紧密结合，这些计算方法可以：（1）采用最新的系统辨识方法，以识别和提取神经状态和动力学规律控制它们与外部输入的相互作用;（2）在天文数字中，时空刺激模式，预测可以最好地完善模型的有趣模式，产生概念洞察力，并产生最佳控制皮层电路动态的能力，具有潜在的临床意义。这种技术和计算的结合将使下一代实验能够让我们学习皮质回路的动态语言（状态空间动态），回放了这种变化的修改版本。语言的洞察力和控制，并了解这种语言是如何从协调一致的活动出现，跨层的多种细胞类型。我们的技术/计算平台将在多个实验中得到验证跨物种和大脑区域，由内部状态动力学的深层和长期理论指导，计算神经科学在整个过程中，新方法将在不同的环境中进行协作验证。我们的实验室的准备工作（此类交叉互动以蓝色文本显示）。我们尤其将集中于测试神经计算的几个基本类别的基础理论：（1）感觉的能力网络通过检测和放大自发的弱感官输入来产生准确的感知背景活动;（2）贝叶斯整合多感官输入，将感觉运动经验转换为外部状态变量及其不确定性的内部估计;（3）触发和维持能够控制稳定行为的离散内部吸引子状态。