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

• 批准号: 10698364
• 负责人: Karl A. Deisseroth
• 金额: $ 6万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10698364
• 关键词: 3-Dimensional; BRAIN initiative; Back; Bayesian Analysis; Behavior; Behavioral; Brain; Brain region; Cells; Chemistry; Clinical; Cognition; Collaborations; Communities; Computer Models; Computing Methodologies; Data; Diagnosis; Dimensions; Education and Outreach; Ensure; Environment; Esthesia; Foundations; Functional disorder; Hydrogels; Image; Individual; Injury; Language; Laws; Learning; Macaca; Maintenance; Measurement; Measures; Medial; Methods; Modeling; Molecular; Monitor; Monkeys; Motor; Mus; Neuraxis; 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; base; cell assembly; cell type; clinically relevant; computational neuroscience; computational platform; computerized tools; control theory; design; experience; experimental study; feeding; frontal eye fields; insight; meetings; millisecond; multimodality; multisensory; neural circuit; neural model; neuropsychiatric disorder; new technology; next generation; nonhuman primate; outreach; relating to nervous system; sensory input; single cell sequencing; social; spatiotemporal; success; technology development; temporal measurement; theories; two-photon

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.

总体而言 - 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 项目概要 神经科学的一个核心挑战涉及了解皮层神经元的组装方式，其中包括 不同的细胞类型和居住在不同的层，共同产生连贯的动态内部状态， 然后与外部感官输入相互作用，时刻生成状态相关的行为 应对这一基本挑战的主要障碍包括缺乏足够的技术和能力。 用于监测、控制、识别和建模皮质细胞神经状态动态的计算工具 我们建议开发一种前所未有的融合。 通过建立我们团队之前的重要工作来实现这种能力的技术和计算。 特别是，我们的技术和计算平台相结合将使我们能够：（1）执行体积成像 在行为过程中收集数千个皮质细胞，以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时空模式 使用 2 光子多 SLM 方法以毫秒时间分辨率识别细胞；以及 (3) 使用水凝胶 组织化学和单细胞测序方法，同时获得深层分子细胞类型信息 在行为过程中同时测量和控制神经元。 读/写/单元打字技术将与计算方法紧密集成，这些方法可以：（1）采用 最先进的系统识别方法来识别和提取神经状态和动力学规律 管理它们与外部输入的相互作用；以及（2）在可能的天文数字中 时空刺激模式，预测可以最好地完善模型的有趣模式，产生概念性的 洞察力，并产生对皮质回路动力学进行最佳控制的能力，具有潜在的临床意义。 这种技术和计算的结合将为下一代实验提供支持，让我们能够学习 皮质回路的动态语言（就状态空间动力学而言），回放其修改版本 具有洞察力和控制力的语言，并理解这种语言是如何从协调一致的活动中产生的 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，以深刻而长期的内部状态动力学理论为指导 自始至终，新方法将在不同领域得到协作验证。 我们的实验实验室的准备工作（这种交叉相互作用以蓝色文本显示）。 将重点测试几个神经计算基础类别的理论：（1）感觉能力 网络通过检测和放大自发的微弱感觉输入来生成准确的感知 背景活动；（2）多感官输入的贝叶斯整合，将感觉运动经验转化为 外部状态变量及其不确定性的内部估计；以及（3）触发和维持 能够控制稳定行为的离散内部吸引子状态。