Multiscale computational frameworks for integrating large-scale cortical dynamics, connectivity, and behavior

用于集成大规模皮层动力学、连接性和行为的多尺度计算框架

基本信息

  • 批准号:
    10840682
  • 负责人:
  • 金额:
    $ 69.14万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-05-12 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

Project Summary/Abstract A central problem in neuroscience is to understand how activity arises from neural circuits to drive animal behaviors. Solving this problem requires integrating information from multiple experimental modalities and organization levels of the nervous system. While modern neurotechnologies are generating high-resolution maps of the brain-wide neural activity and anatomical connectivity, novel theoretical frameworks are urgently needed to realize the full potential of these datasets. Most state-of-the-art methods for analyzing high-dimensional data are based on detecting correlations in neural activity and do not provide links to the underlying anatomical connectivity and circuit mechanisms. As a result, conclusions derived with these methods rarely generalize across different behaviors and are hard to validate in perturbation experiments. In contrast, mechanistic theories, which combine connectivity, activity, and function, have been highly successful in understanding function of small neural circuits. Conditions under which insights from small circuits scale to large distributed circuits have not been explored. Mechanistic theories informed by multiple data modalities are critically missing to guide experiments probing global neural dynamics on the brain-wide scale. The main goal of this proposal is to develop computational frameworks for modeling global neural dynamics, which utilize anatomical connectivity and predict rich behavioral outputs on single trials. Our project will address two complementary aims. First, we will take advantage of recently available datasets of high-resolution brain- wide neural activity and anatomical connectivity to construct a multiscale model of functional dynamics across the mouse cortex. Integrating measurements across multiple scales, from mesoscopic to near-cellular resolution, we aim to reveal the effective degrees of freedom at each scale, which constrain global neural dynamics and drive rich patterns of behavior. Second, we will leverage techniques from dynamical systems theory and artificial recurrent neural networks to develop circuit reduction methods that infer interpretable low-dimensional circuit mechanisms of cognitive computations from high-dimensional neural activity data. Rather than merely detecting correlations, our method infers the structural connectivity of an equivalent low-dimensional circuit that fits projections of high-dimensional neural activity data and implements the behavioral task. We will apply this method to multi-area neural activity recordings from behaving animals to reveal distributed circuit mechanisms of context-dependent decision making. The computational frameworks developed in this proposal can be validated in perturbation experiments and extended to other nervous systems and behaviors.
项目总结/文摘

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Predictive variational autoencoder for learning robust representations of time-series data
  • DOI:
    10.48550/arxiv.2312.06932
  • 发表时间:
    2023-12
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Julia Huiming Wang;Dexter Tsin;Tatiana Engel
  • 通讯作者:
    Julia Huiming Wang;Dexter Tsin;Tatiana Engel
The dynamics and geometry of choice in premotor cortex.
前运动皮层的动力学和几何结构选择。
  • DOI:
    10.1101/2023.07.22.550183
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Genkin,Mikhail;Shenoy,KrishnaV;Chandrasekaran,Chandramouli;Engel,TatianaA
  • 通讯作者:
    Engel,TatianaA
Choice selective inhibition drives stability and competition in decision circuits.
  • DOI:
    10.1038/s41467-023-35822-8
  • 发表时间:
    2023-01-10
  • 期刊:
  • 影响因子:
    16.6
  • 作者:
    Roach, James P.;Churchland, Anne K.;Engel, Tatiana A.
  • 通讯作者:
    Engel, Tatiana A.
The diversity and specificity of functional connectivity across spatial and temporal scales.
  • DOI:
    10.1016/j.neuroimage.2021.118692
  • 发表时间:
    2021-12-15
  • 期刊:
  • 影响因子:
    5.7
  • 作者:
    Engel, Tatiana A.;Schoelvinck, Marieke L.;Lewis, Christopher M.
  • 通讯作者:
    Lewis, Christopher M.
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Tatiana Engel其他文献

Tatiana Engel的其他文献

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{{ truncateString('Tatiana Engel', 18)}}的其他基金

Multiscale computational frameworks for integrating large-scale cortical dynamics, connectivity, and behavior
用于集成大规模皮层动力学、连接性和行为的多尺度计算框架
  • 批准号:
    10263628
  • 财政年份:
    2021
  • 资助金额:
    $ 69.14万
  • 项目类别:
Discovering dynamic computations from large-scale neural activity recordings
从大规模神经活动记录中发现动态计算
  • 批准号:
    10002240
  • 财政年份:
    2018
  • 资助金额:
    $ 69.14万
  • 项目类别:
Discovering dynamic computations from large-scale neural activity recordings
从大规模神经活动记录中发现动态计算
  • 批准号:
    9789277
  • 财政年份:
    2018
  • 资助金额:
    $ 69.14万
  • 项目类别:

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