Project 5: Analysis

项目概要：项目5，神经数据的分析和建模 工作记忆是一种在头脑中暂时保存多条信息以进行操作的能力， 对几乎所有认知能力都至关重要这一多方面的研究项目旨在全面 在多个大脑区域中剖析这种能力的神经回路机制。大量的人口记录， 例如，将在本提案的其他组成部分中获得的信息，为评估 在单次试验的基础上，每时每刻的大脑状态动态。然而，它们的规模和复杂性 挑战，以及将收集的各种数据，包括解剖学，行为，神经活动， 和扰动。该项目将开发和应用新的统计分析和建模方法， 迎接这些挑战。神经群体活动中的大部分变化通常由以下因素主导： 少数变量的变化，称为“潜在变量”。这个项目将利用非常 大型数据集，收集在该项目的其他组成部分，许多同时记录的神经元， 开发先进的线性和非线性方法，以确定最具信息量的潜在变量。分析 这些数据集，研究人员将开发新的潜在变量发现方法。首先，他们将联合收割机 高级定量行为分析和高级统计神经分析。第二，他们将联合收割机 用广义线性模型拟合神经数据发现潜在空间。由此产生的非线性方法 将提供前所未有的完整的数据统计描述：这些方法旨在 同时发现和捕捉最重要的潜在变量的动态，并产生一个 每个单独记录的神经元的反应的完整统计特征。在生物物理建模中 工作，关键是创造一个机械的理解在神经回路水平，这个项目将开发和 测试工作记忆和决策过程中局部和多脑区活动的模型。这些 模型将建立在严格的敏感性分析技术之上，用于识别关键的网络交互作用 潜在的观察行为。这些模型将用于解释现有数据，并最大限度地设计 关于区域间网络互动的信息实验，它们将提供一个原则性的平台， 从中设计未来的实验，测试关于功能的特定假设，并进一步限制 模型

Project Summary: Project 5, Analysis and Modeling of Neural Data Working memory, the ability to temporarily hold multiple pieces of information in mind for manipulation, is central to virtually all cognitive abilities. This multi-component research project aims to comprehensively dissect the neural circuit mechanisms of this ability across multiple brain areas. Large population recordings, such as those that will be obtained in other components of this proposal, open the door to assessing the dynamics of brain states on a single-trial, moment-by-moment basis. Yet their size and complexity present a challenge, as does the variety of data that will be collected, incorporating anatomy, behavior, neural activity, and perturbations. This project will develop and apply novel statistical analyses and modeling approaches to meet these challenges. The lion’s share of the variance in neural population activity is often dominated by variations in a small number of variables, which are called “latent variables.” This project will leverage the very large data sets, collected in other components of the project, of many simultaneously recorded neurons to develop advanced linear and nonlinear methods to identify the most informative latent variables. To analyze these datasets, the researchers will develop new latent variable discovery methods. First, they will combine advanced quantitative behavioral analysis with advanced statistical neural analysis. Second, they will combine latent space discovery with fitting of generalized linear models to neural data. The resulting nonlinear methods will provide an unprecedentedly complete statistical description of the data: these methods aim to simultaneously discover and capture the dynamics of the most important latent variables, and to produce a full statistical characterization of the responses of each individual recorded neuron. In biophysical modeling work, critical to creating a mechanistic understanding at the neural circuit level, this project will develop and test models of both local and multi-brain-region activity during working memory and decision-making. These models will build upon rigorous sensitivity-analysis techniques for identifying the critical network interactions underlying observed behavior. The models will be used both to interpret existing data and to design maximally informative experiments about inter-regional network interactions, and they will provide a principled platform from which to design future experiments that test specific hypotheses about function and further constrain the models.