Project 5: Analysis

项目5：分析

• 批准号: 10247569
• 负责人: Jonathan William Pillow
• 金额: $ 42.96万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-28 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10247569
• 关键词: Anatomy; Architecture; Area; Behavior; Behavioral; Biophysics; Brain; Brain region; Code; Cognitive; Complement; Data; Data Set; Decision Making; Dimensions; Future; Heterogeneity; Individual; Linear Models; Literature; Methods; Mind; Modeling; Neurons; Population; Property; Recording of previous events; Recurrence; Reproducibility; Research Personnel; Research Project Grants; Rewards; Short-Term Memory; Space Models; Statistical Data Interpretation; Statistical Models; Techniques; Testing; Time; Training; Variant; Work; behavioral response; biophysical model; cell type; cognitive ability; cognitive task; design; experimental study; interest; large datasets; network models; neural circuit; neural model; neuromechanism; novel; relating to nervous system; response; sensory stimulus; virtual

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.

项目摘要：项目5，神经数据的分析和建模 工作记忆，暂时牢记多个信息进行操作的能力是 几乎所有认知能力的核心。这个多组分研究项目旨在全面 在多个大脑区域剖析了这种能力的神经回路机制。人口记录庞大， 例如将在该提案的其他组成部分中获得的，打开门以评估 大脑状态的动力学逐矩。然而，它们的大小和复杂性呈现 挑战，将收集的各种数据，结合解剖学，行为，神经活动， 和扰动。该项目将开发并应用新颖的统计分析和建模方法 应对这些挑战。狮子在神经种群活动中的差异通常由 少数变量的变化称为“潜在变量”。该项目将利用 大型数据集收集在项目的其他组件中，许多简单地记录了神经元 开发高级线性和非线性方法，以识别最有用的潜在变量。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，他们会结合 先进的定量行为分析，具有高级统计神经分析。其次，他们将结合 潜在空间发现，将广义线性模型与神经数据拟合。产生的非线性方法 将为数据提供前所未有的完整统计描述：这些方法旨在 类似地发现并捕获最重要的潜在变量的动态，并产生一个 每个记录神经元的反应的完整统计表征。在生物物理建模中 工作，对于在神经电路层面建立机械理解至关重要的工作，该项目将发展和 在工作记忆和决策过程中，本地和多脑区活动的测试模型。这些 模型将基于严格的灵敏度 - 分析技术来识别关键网络交互 基本观察到的行为。这些模型将既可以解释现有数据又设计最大 有关区域间网络互动的信息丰富的实验，它们将提供一个主要的平台 从哪个设计实验，以检验有关功能的特定假设并进一步限制 型号。