Connecting neural circuit architecture and experience-driven probabilistic computations

连接神经电路架构和经验驱动的概率计算

基本信息

  • 批准号:
    10007281
  • 负责人:
  • 金额:
    $ 77.24万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-09-23 至 2024-09-22
  • 项目状态:
    已结题

项目摘要

Project Summary: Organisms' actions and decisions are guided by experience. Models of such behavior often appeal to the formalism of probabilistic inference, in which expectations about the world build up sequentially due to past observations. These models can account for typical response patterns of subjects performing cog- nitive tasks. However, a theory grounded in biophysical principles of neural circuit architecture and activity is lacking. Our proposal seeks to fill this gap by constructing mechanistic neural circuit models of probabilistic infer- ence, which we will validate using innovative computational tools for matching the statistics of neural population recordings and subject behavior to the outputs of high-dimensional models. Our proposed work will address several outstanding questions concerning how neural circuits are guided by experience. Neural architecture likely plays a role in the brain's probabilistic computations, but there is not yet a clear theory of this connection. We propose that plasticity-driven changes in neural circuit architecture underlie these computations by reshaping the probability space of neural activity patterns. Neural activity is therefore biased to encode more likely beliefs, in light of experience. Our framework demonstrates this clearly using innovative mathematical methods to extract the low-dimensional activity dynamics of neural circuits subject to plasticity with various timescales. This approach will be applied to interpret our collaborators' data from subjects performing tasks in which they must estimate a remembered variable after a time delay. Theory is also lacking concerning how dynamics of neural activity represent variables that relevant to a cog- nitive tasks spanning multiple timescales. Most studies consider cleanly structured networks or purely random networks, producing fairly stereotypical neural population activity patterns. We will test the computational capa- bilities of plastic networks with mixed structured and random connectivity, focusing on how the resulting neural population dynamics represent remembered variables. Our neural circuit models will be validated and parame- terized using statistics of (a) neural populations recorded using multielectrodes in non-human primates and (b) subjects' behavioral responses. Our neural circuit models, software and tools used for fitting our models, and data used to validate will be shared widely as a tool kit for use by the broader research community.
项目摘要:生物体的行动和决定是由经验指导的。这种行为的模式通常 诉诸于概率推理的形式主义,其中对世界的期望是按顺序建立的 由于过去的观察。这些模型可以解释执行cog的受试者的典型反应模式, 完成任务。然而,基于神经回路结构和活动的生物物理学原理的理论, 缺乏我们的建议试图通过构建概率推断的机械神经电路模型来填补这一空白。 我们将使用创新的计算工具进行验证,以匹配神经种群的统计数据。 记录和主题行为的高维模型的输出。 我们提出的工作将解决几个悬而未决的问题,关于神经回路是如何被引导的, 体验.神经结构可能在大脑的概率计算中发挥作用,但目前还没有一个 这种联系的明确理论。我们认为,可塑性驱动的神经回路结构的变化, 这些计算通过重塑神经活动模式的概率空间。因此,神经活动 根据经验,偏向于编码更可能的信念。我们的框架清楚地表明了这一点, 创新的数学方法来提取神经回路的低维活动动力学, 不同时间尺度的可塑性。这种方法将被应用于解释我们的合作者从受试者的数据 执行任务,在这些任务中,他们必须在一段时间延迟后估计一个记忆中的变量。 关于神经活动的动态如何代表与齿轮相关的变量,也缺乏理论。 跨多个时间刻度的任务。大多数研究考虑干净的结构网络或纯粹随机的网络 网络,产生相当刻板的神经群体活动模式。我们将测试计算能力- 具有混合结构化和随机连接性的可塑网络的能力,重点关注由此产生的神经网络如何 种群动态代表记忆变量。我们的神经回路模型将被验证和参数化- 使用(a)在非人灵长类动物中使用多电极记录的神经群体和(B)的统计数据进行特征化 受试者的行为反应我们的神经回路模型、用于拟合模型的软件和工具,以及 用于验证的数据将作为工具包广泛分享,供更广泛的研究界使用。

项目成果

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