Mechanisms of neural circuit dynamics in working memory anddecision-making

工作记忆和决策中的神经回路动力学机制

基本信息

  • 批准号:
    9983177
  • 负责人:
  • 金额:
    $ 306.24万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-09-28 至 2022-07-31
  • 项目状态:
    已结题

项目摘要

Project Summary Working memory, the ability to temporarily hold multiple pieces of information in mind for manipulation, is central to virtually all cognitive abilities. Recent technical advances have opened an unprecedented opportunity to comprehensively dissect the neural circuit mechanisms of this ability across multiple brain areas. The task to be studied is a common form of decision-making that is based on the gradual accumulation of sensory evidence and thus relies on working memory. A team of leading experts propose to investigate the neural basis of this behavior using the latest techniques, including virtual reality, high-throughput automated behavioral training, large-scale cellular-resolution imaging in behaving rodents, manipulation of neural activity in specific brain areas and cell types, and automated anatomical reconstruction. In particular, the researchers will identify key brain regions that are required for this decision task through systematic, temporally specific inactivations via optogenetics technology, across all of dorsal cortex and in key subcortical areas, and use quantitative model-fitting to evaluate the effects. They will use state-of-the-art two-photon calcium imaging methods and electrophysiology to characterize the information flow in many individual neurons within these brain areas during the task. In addition, they will use cutting-edge anatomical reconstructions and new functional connectivity methods, within and across brain regions, to evaluate the interactions of these physiologically characterized neurons. The long-term goal of this project is to arrive at a complete, brain-wide understanding of the cellular and circuit mechanisms of activity dynamics related to working memory. Finally, they will use sophisticated computational methods to incorporate this new understanding into a realistic circuit model that will support a tightly integrated process of model-guided experimental design, in which the model suggests the most informative experiments and their results are then fed back to improve the model’s fidelity. This process is expected to produce the most accurate and detailed multi-brain-region biophysical circuit model of a cognitive process in existence. In addition, the proposed research will enable researchers to generate and test a variety of hypotheses about the neural basis of evidence accumulation, working memory, and decision-making. Taken together, these achievements will represent a crucial step toward a mechanistic understanding of how the brain works with information.
项目摘要 工作记忆,暂时牢记多个信息进行操作的能力是 几乎所有认知能力的核心。最近的技术进步已经开放了前所未有的 有机会全面剖析多个大脑的这种能力的神经回路机制 区域。要研究的任务是一种基于等级的决策的常见形式 感觉证据的积累,因此依赖于工作记忆。一支领先的专家团队提议 使用最新技术(包括虚拟现实)来研究这种行为的神经元基础 高通量自动行为训练,行为啮齿动物中的大规模细胞分辨率成像, 在特定的大脑区域和细胞类型中操纵神经活动,并自动解剖 重建。特别是,研究人员将确定该决定所需的关键大脑区域 通过光遗传学技术通过系统的,暂时特定的灭绝任务,在所有背侧 皮质和关键皮层区域,并使用定量模型拟合来评估效果。他们会使用 最先进的两光子钙成像方法和电生理学来表征信息 任务期间,这些大脑区域内许多单个神经元的流动。此外,他们将使用 尖端的解剖重建和新的功能连接方法在大脑内外 区域,以评估这些物理表征的神经元的相互作用。长期目标 该项目将对细胞和电路机制有完整的脑部了解 活动动态与工作记忆有关。最后,他们将使用复杂的计算方法 将这种新的理解纳入了现实的电路模型,该模型将支持紧密整合 模型引导的实验设计过程,其中模型提出了最有用的信息 然后将实验及其结果反馈,以提高模型的保真度。期望这个过程 产生认知过程中最准确,最详细的多脑区域生物物理电路模型 存在。此外,拟议的研究将使研究人员能够生成和测试各种 关于证据积累,工作记忆和决策的神经基础的假设。 综上所述,这些成就将代表着对机械理解的关键一步 大脑与信息一起工作。

项目成果

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Carlos D Brody其他文献

Carlos D Brody的其他文献

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{{ truncateString('Carlos D Brody', 18)}}的其他基金

P2: Geometry of Neural Representations and Dynamics
P2:神经表征和动力学的几何
  • 批准号:
    10705964
  • 财政年份:
    2023
  • 资助金额:
    $ 306.24万
  • 项目类别:
Mechanisms of neural circuit dynamics in working memory and decision-making
工作记忆和决策中的神经回路动力学机制
  • 批准号:
    10705962
  • 财政年份:
    2023
  • 资助金额:
    $ 306.24万
  • 项目类别:
C3: Behavior Automation
C3:行为自动化
  • 批准号:
    10705970
  • 财政年份:
    2023
  • 资助金额:
    $ 306.24万
  • 项目类别:
C1: Administrative
C1:行政
  • 批准号:
    10705968
  • 财政年份:
    2023
  • 资助金额:
    $ 306.24万
  • 项目类别:
C2: Data Science
C2:数据科学
  • 批准号:
    10705969
  • 财政年份:
    2023
  • 资助金额:
    $ 306.24万
  • 项目类别:
An experimental platform to investigate the neural mechanisms underlying flexible decision-making
研究灵活决策神经机制的实验平台
  • 批准号:
    10366077
  • 财政年份:
    2021
  • 资助金额:
    $ 306.24万
  • 项目类别:
Behavior Automation
行为自动化
  • 批准号:
    9983196
  • 财政年份:
    2017
  • 资助金额:
    $ 306.24万
  • 项目类别:
Administrative Core
行政核心
  • 批准号:
    9983201
  • 财政年份:
    2017
  • 资助金额:
    $ 306.24万
  • 项目类别:
Perturbations and Behavior
扰动和行为
  • 批准号:
    9983190
  • 财政年份:
    2017
  • 资助金额:
    $ 306.24万
  • 项目类别:
Perturbations and Behavior
扰动和行为
  • 批准号:
    10247575
  • 财政年份:
    2017
  • 资助金额:
    $ 306.24万
  • 项目类别:

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Leadership and Administrative Core
领导和行政核心
  • 批准号:
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  • 财政年份:
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    $ 306.24万
  • 项目类别:
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亚裔美国人、夏威夷原住民和太平洋岛民研究与教育 (CARE) 2.0 合作方法
  • 批准号:
    10740342
  • 财政年份:
    2023
  • 资助金额:
    $ 306.24万
  • 项目类别:
A mechanistic and dyadic approach to identify how interpersonal conscientiousness supports cognitive health and lowers risk of dementia
采用机械和二元方法来确定人际责任感如何支持认知健康并降低痴呆风险
  • 批准号:
    10739837
  • 财政年份:
    2023
  • 资助金额:
    $ 306.24万
  • 项目类别:
Earlier-Life Predictors of Midlife Risk Factors for Dementia: A 35-Year Follow-up
中年痴呆症风险因素的早期预测因素:35 年随访
  • 批准号:
    10596295
  • 财政年份:
    2023
  • 资助金额:
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