P2: Geometry of Neural Representations and Dynamics

P2：神经表征和动力学的几何

• 批准号: 10705964
• 负责人: Carlos D Brody
• 金额: $ 35.6万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-08 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705964
• 关键词: Affect; Area; BRAIN initiative; Behavior; Behavioral; Brain; Brain region; Calcium; Cells; Code; Cognitive; Data; Data Analyses; Data Science; Data Set; Decision Making; Differential Equation; Dimensions; Dorsal; Etiology; Foundations; Funding; Generations; Geometry; Goals; Hippocampus; Image; Individual; Learning; Measures; Memory; Methods; Modeling; Mus; Neocortex; Neuroanatomy; Neurons; Optics; Physiological; Population; Positioning Attribute; Prefrontal Cortex; Property; Psychological reinforcement; Resolution; Short-Term Memory; Structure; Testing; Work; design; experimental study; information organization; insight; neocortical; network architecture; neural; neural circuit; neural model; neuromechanism; new technology; optogenetics; predictive modeling; preservation; programs; scaffold; statistical learning; success; virtual reality

Project Summary/Abstract: Project 2, Geometry of Neural Representations and Dynamics In this project, we will measure and model the geometry of neural coding and dynamics during an evidence-accumulation decision task. This work will advance the overall goal of this U19 program—elucidating the circuit mechanisms that underlie working memory and decision-making and applying this information to construct a multi-region, mechanistic circuit model. Neural population activity in the dorsal hippocampus of mice is constrained to lie on a low-dimensional manifold during our task. Important behavioral variables like position along the maze, and learned cognitive variables like evidence, are represented as gradients in different directions along this latent space, giving rise to a geometric representation of knowledge. Observed neural activity sequences correspond to trial-specific trajectories along the manifold. The first aim will characterize geometric representations in hippocampus and neocortex, to identify general encoding principles of these representations and provide a richer dataset for comparison with our mechanistic models. We will compare firing fields and manifold structure to predictions from several existing statistical learning models to test the general idea that the manifolds capture task-specific statistical regularities. We will also characterize geometric properties of neural coding and manifold structure across these areas, starting with the prefrontal cortex, using simultaneous Neuropixels recordings from multiple regions. We will identify what attributes, such as intrinsic dimensionality and variable encodings, are preserved in the cortex compared to hippocampal representations. The second aim will evaluate the neural dynamics that govern state space flow along the manifold. So far, we have focused on inferring the geometry of neural representations and have not directly examined dynamics on the manifold. We developed a nonlinear method to simultaneously estimate manifold dimensions and the dynamics on that manifold, based on neural spike data. We will extend this method for use with calcium imaging data and then apply it to spiking and imaging data from other projects to infer manifold state space flow. These data-driven inferences will be compared to the dynamics predicted by existing models. Finally, the third aim will causally probe manifold structure and the mechanisms of sequence generation using optogenetic perturbation. With our new technology for simultaneous optogenetic perturbation and imaging, we will measure changes in neural population activity during multi-neuron perturbations that will be designed using sequence and manifold structure derived from population imaging data. Data, analyses, and modeling from this project will provide key insights into the general properties of neural manifolds in a variety of brain regions, along with the flow-field dynamics along manifolds that define neural trajectories on individual trials. Taken together, these experiments and models will substantially advance three priority areas of the BRAIN Initiative: the brain in action, demonstrating causality, and identifying fundamental principles.

项目概要/摘要：项目2，神经表示和动力学的几何 在本项目中，我们将测量并建模神经编码和动态的几何形状 证据积累决策任务。这项工作将推进这一U19计划的总体目标-阐明 作为工作记忆和决策基础的电路机制，并将这些信息应用于 构建一个多区域的机械电路模型。大鼠背侧海马神经元群体活动的研究 在我们的任务中，老鼠被限制在低维流形上。重要的行为变量，如 沿着迷宫沿着的位置，和学习的认知变量，如证据，表示为梯度， 沿着这个潜在的空间沿着不同的方向，产生了知识的几何表示。观察到 神经活动序列对应于沿流形沿着的试验特定轨迹。 第一个目标将描述海马和新皮层的几何表征，以识别 这些表示的一般编码原则，并提供更丰富的数据集与我们的 机械模型我们将比较射击场和管汇结构与几个现有的预测 统计学习模型来测试流形捕获特定任务的统计信息的一般思想 - 是的我们还将描述神经编码和流形结构的几何特性， 这些区域，从前额皮质开始，使用来自多个神经元的同步记录， 地区我们将确定保留了哪些属性，例如内在维度和变量编码 与海马体的表现相比。 第二个目标将评估神经动力学，管理状态空间流沿着流形。所以 到目前为止，我们一直专注于推断神经表征的几何结构，并没有直接检查 流形上的动力学我们发展了一种非线性方法来同时估计流形维数 以及流形上的动力学，基于神经尖峰数据。我们将扩展此方法， 钙成像数据，然后将其应用于其他项目的尖峰和成像数据，以推断多种状态 空间流动这些数据驱动的推断将与现有模型预测的动态进行比较。 最后，第三个目标将对流形结构和序列生成机制进行因果性的探讨 使用光遗传学扰动。利用我们的新技术， 通过成像，我们将测量多神经元扰动期间神经群体活动的变化， 使用从群体成像数据导出的序列和流形结构来设计。数据、分析和 从这个项目的建模将提供关键的洞察神经流形的一般性质，在各种 大脑区域，沿着流场动力学沿着流形，定义个体上的神经轨迹， 审判总之，这些实验和模型将大大推进《公约》的三个优先领域。 大脑倡议：大脑在行动，证明因果关系，并确定基本原则。