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NCS-FO: Variability and the Global Brain

NCS-FO：变异性和全球大脑

基本信息

批准号：
2024364
负责人：
Eric Shea-Brown
金额：
$ 99.96万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024364&HistoricalAwards=false
关键词：
NCS FO Variability Global Brain

项目摘要

While the brain's computational abilities are in many respects unrivaled, its workings appear to be far noisier than those of almost any engineered computational system. Even in carefully controlled experiments in which the same conditions are presented over multiple trials, neural activity is strikingly variable from one trial to the next. This project aims to resolve the apparent contradiction between the brain's computational proficiency and its apparently high levels of noise. The core hypothesis is that much of the observed neural variability is driven not by noise but by internal brain modes -- that is, by coordinated patterns of activity across the brain. Thus, variability may be a signature of dynamic and uniquely biological computations rather than noisy fluctuations. If this hypothesis is correct, then observation of these global modes should explain choices that subjects make in behavioral tasks, and perturbation of the modes should alter their patterns of choices in systematic ways. To test this hypothesis, the project employs a novel joint experimental and theoretical approach to measure the variability in brain-wide neural activity across scales, to define its relationship to behavior, and to dynamically perturb these modes to impact behavioral performance both within and across individuals. To build a transformative understanding of the link between neural and behavioral variability, the project will use multi-probe Neuropixels technology that enables simultaneous recording at submillisecond resolution from thousands of individual neurons distributed across the brain, coupled with advanced data analytic and dynamical modeling tools to extract activity modes from these data. These analyses will be performed together with behavioral assays that probe multiple aspects of behavioral performance, including engagement, perceptual sensitivity, and vigor. Statistical modeling will then be used to identify the functional role of these modes in regulating behavioral performance, and how their activity drives behavioral differences both across trials and across individuals. Beyond correlative analysis, control theory tools will design patterned optogenetic perturbations to provide direct causal tests of this novel functional role for brain-wide activity modes. If the project succeeds, the result will be a new understanding of the nature of the ongoing fluctuations in brain-wide activity patterns trial by trial and individual by individual in terms of behavior rather than noise -- a key step in deciphering the logic of distributed computation underlying perception and cognition. The project will develop and disseminate novel open data and code to impact research and training nationwide. It will also build a dynamic, team-mentored environment led by investigators from very distinct disciplines -- from neuroscience to applied mathematics to control engineering -- which will prepare both undergraduate and graduate trainees to bridge disciplines and scientific cultures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

虽然大脑的计算能力在许多方面是无与伦比的，但它的工作似乎比几乎任何工程计算系统都要嘈杂得多。即使在多次试验中呈现相同条件的精心控制的实验中，神经活动在一次又一次的试验中也是惊人的不同。该项目旨在解决大脑的计算能力和其明显的高噪音之间的明显矛盾。核心假设是，大部分观察到的神经变异性不是由噪音驱动的，而是由大脑内部模式驱动的--也就是说，由整个大脑的协调活动模式驱动。因此，可变性可能是动态和独特的生物计算的特征，而不是噪声波动。如果这一假设是正确的，那么对这些全局模式的观察应该解释受试者在行为任务中做出的选择，而这些模式的扰动应该以系统的方式改变他们的选择模式。为了验证这一假设，该项目采用了一种新的联合实验和理论方法来衡量整个大脑神经活动在不同尺度上的可变性，定义其与行为的关系，并动态地扰动这些模式，以影响个体内部和跨个体的行为表现。为了建立对神经和行为变异性之间联系的变革性理解，该项目将使用多探头神经像素技术，该技术能够以亚毫秒的分辨率同时记录分布在大脑中的数千个单个神经元，并结合先进的数据分析和动态建模工具从这些数据中提取活动模式。这些分析将与探索行为表现的多个方面的行为分析一起进行，包括参与度、感知敏感度和活力。然后将使用统计建模来确定这些模式在调节行为表现方面的功能作用，以及它们的活动如何在试验和个体之间导致行为差异。除了相关分析，控制理论工具将设计图案化的光遗传扰动，为全脑活动模式的这种新的功能角色提供直接的因果测试。如果该项目成功，结果将是对全脑活动模式持续波动的本质有了新的理解，从行为而不是噪音的角度进行逐一试验和个人试验--这是破译感知和认知背后的分布式计算逻辑的关键一步。该项目将开发和传播新的开放数据和代码，以影响全国的研究和培训。它还将建立一个动态的、团队指导的环境，由来自非常不同学科的研究人员领导--从神经科学到应用数学再到控制工程--这将为本科生和研究生培养学科和科学文化之间的桥梁做好准备。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。