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.

项目摘要/摘要：项目2，神经表示和动态的几何形状 在这个项目中，我们将测量和建模神经编码和动态的几何形状 证据积累的决策任务。这项工作将促进该U19计划的总体目标 - 恢复 基于工作记忆和决策以及将此信息应用于的电路机制 构建多区域机械电路模型。背侧海马的神经人口活动 在我们的任务期间，小鼠被限制在低维歧管上。重要的行为变量，例如 沿着迷宫的位置和学到的认知变量（如证据）表示为梯度 沿这个潜在空间的不同方向，导致知识的几何表示。观察到 神经活动序列对应于沿流形的试验特异性轨迹。 第一个目的将表征海马和新皮层中的几何表示 这些表示形式的一般编码原则，并提供了更丰富的数据集，以与我们的 机械模型。我们将将射击场和多种结构与几个现有的预测进行比较 统计学习模型，以测试流形捕获特定任务统计的一般思想 规律。我们还将表征跨神经编码和歧管结构的几何特性 这些区域从前额叶皮层开始，使用来自多个的简单神经偶像记录 地区。我们将保留哪些属性，例如内在维度和可变编码 与海马表示相比，在皮层中。 第二个目标将评估控制沿流动状态空间流动的神经动力学。所以 到目前为止，我们专注于推断神经表示的几何形状，但没有直接检查 多种动态。我们开发了一种非线性方法来简单地估计歧管尺寸 以及基于神经尖峰数据的该歧管的动力学。我们将扩展此方法用于 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歧管状态 空间流。这些数据驱动的推论将与现有模型预测的动态进行比较。 最后，第三个目标将因果探测歧管结构和序列产生的机制 使用光遗传扰动。使用我们的新技术，用于同时光遗传扰动和 成像，我们将在多神经元扰动期间衡量神经种群活动的变化 使用序列和歧管结构设计，这些结构从人口成像数据中得出。数据，分析和 该项目的建模将为各种神经流形的一般特性提供关键的见解 大脑区域，以及沿流动的流动动力学，这些动力学定义了个体的神经元轨迹 试验。综上所述，这些实验和模型将第二次提高三个优先领域 大脑倡议：动作中的大脑，证明因果关系并确定基本原理。