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; 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 - 外部输入与内部动力学的相互作用： 大脑状态对神经功能和行为的影响 项目摘要 神经科学中的一个核心挑战涉及了解皮质神经元组合的完成 不同的细胞类型和居住在不同层的层次，共同生成相干的动态内部状态， 然后与外部感觉输入相互作用，以逐矩产生状态依赖的行为 基础。应对这一基本挑战的关键障碍包括缺乏足够的技术和 从皮质细胞中出现的计算工具以监视，控制，识别和建模神经元状态动力学 跨越多个皮质细胞类型和层的组件。我们建议开发前所未有的融合 通过建立团队的重要工作来建立技术和计算以实现此类功能的计算。 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 行为过程中成千上万的皮质细胞收集相关的空间时间活动模式和3D 定位； （2）只需将任意的空间时间模式写入数十亿个单独的时间 使用2光子Multislm方法以毫秒临时分辨率鉴定出细胞； （3）使用水凝胶 组织化学和单细胞测序方法，在相同 在行为过程中均测量和控制的神经元。这个前所未有的同时发生 读取/写入/单元格技术将与可以：（1）采用的计算方法紧密集成 识别和提取神经状态和动态定律的最先进的系统识别方法 管理他们与外部输入的互动； （2）在可能的天文数字中 时空刺激模式，预测有趣的刺激模式，可以最好地完善模型，产生概念 洞察力，并产生具有潜在临床相关性的皮质电路动力学最佳控制能力。 这种结合的技术和计算将增强下一代实验，使我们能够学习 皮质电路的动态语言（就状态空间动力学而言），播放此版本的修改版本 洞察力和控制的语言，并了解该语言如何从一致的活动中出现 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，以内部状态动态的深层和长期的理论为指导 计算神经科学。在整个过程中，新方法将在潜水员中进行协作验证 我们的实验实验室的制备（这种交叉切割相互作用在蓝色文本中显示）。特别是我们 将着重于测试几个基础类别的神经计算基础类别的理论：（1）感觉的能力 网络通过检测和放大弱的感觉输入来产生准确的知觉 背景活动； （2）多感觉输入的贝叶斯整合以将感觉运动体验转换为 外部状态变量及其不确定性的内部估计； （3）触发和维护 能够控制稳定行为的离散内部吸引力状态。