Project 5: Analysis

项目总结：项目5，神经数据的分析和建模 工作记忆，即在脑海中暂时记住多个信息以进行操作的能力，是 几乎是所有认知能力的核心。这一多组分研究项目旨在全面 剖析这种能力在多个大脑区域的神经回路机制。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那些内容，为评估 在单次试验的基础上，每一时刻的大脑状态的动态变化。然而，它们的规模和复杂性呈现出一种 挑战，就像将收集的各种数据一样，包括解剖、行为、神经活动、 和微扰。该项目将开发和应用新的统计分析和建模方法，以 迎接这些挑战。神经种群活动的最大份额的方差通常由 少数变量的变化，这被称为“潜变量”。这个项目将充分利用 在该项目的其他组件中收集的大型数据集，其中包括许多同时记录的神经元 发展先进的线性和非线性方法来识别信息最丰富的潜在变量。要分析 在这些数据集上，研究人员将开发新的潜在变量发现方法。首先，他们将联合起来 高级量化行为分析和高级统计神经分析。第二，他们将联合起来 用广义线性模型对神经数据进行拟合的潜在空间发现。由此产生的非线性方法 将提供史无前例的完整数据统计描述：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 对每个单独记录的神经元的反应进行完整的统计表征。在生物物理建模中 这项工作对在神经回路水平上建立机械性理解至关重要，该项目将开发和 测试工作记忆和决策过程中局部和多个脑区活动的模型。这些 模型将建立在用于识别关键网络交互的严格敏感度分析技术的基础上 潜在观察到的行为。这些模型将用于解释现有数据和最大限度地进行设计 关于区域间网络交互的信息性实验，它们将提供一个原则性的平台 以此来设计未来的实验，以测试关于功能的特定假设并进一步限制 模特们。

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.