Connecting neural circuit architecture and experience-driven probabilistic computations

项目总结：生物体的行动和决定是由经验指导的。这类行为的典范经常 诉诸于概率推理的形式主义，在这种推理中，人们对世界的期望是按顺序建立起来的 由于过去的观察。这些模型可以解释受试者的典型反应模式。 无目的的任务。然而，一个基于神经电路结构和活动的生物物理学原理的理论是 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fi消除这一差距。 ENCE，我们将使用创新的计算工具来匹配神经种群的统计数据进行验证 高维模型输出的记录和受试者行为。 我们提议的工作将解决几个悬而未决的问题，即神经回路是如何由 经验。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 关于这一联系的明确理论。我们认为，神经电路结构的可塑性驱动的变化是基础 这些计算通过重塑神经活动模式的概率空间来实现。因此，神经活动 根据经验，偏向于编码更可能的信念。我们的框架使用以下命令清楚地演示了这一点 用创新的数学方法提取神经回路的低维活动动力学 具有不同时标的可塑性。这种方法将被应用于解释我们的合作者从受试者那里获得的数据 执行任务时，他们必须在一段时间延迟后估计记忆中的变量。 关于神经活动的动力学如何代表与齿轮相关的变量，也缺乏理论。 跨越多个时间尺度的主动任务。大多数研究认为结构清晰的网络或纯粹的随机网络 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- 具有混合结构和随机连通性的塑料网络的可能性，重点关注由此产生的神经 人口动态代表了记忆中的变量。我们的神经电路模型将得到验证和参数- 使用(A)在非人类灵长类动物中使用多电极记录的神经种群和(B)的统计数据来描述的 受试者的行为反应。我们的神经电路模型、用于fi设置模型的软件和工具，以及 用于验证的数据将作为工具包广泛共享，供更广泛的研究界使用。

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.