Multiscale computational frameworks for integrating large-scale cortical dynamics, connectivity, and behavior

用于集成大规模皮层动力学、连接性和行为的多尺度计算框架

• 批准号: 10263628
• 负责人: Tatiana Engel
• 金额: $ 62.14万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-01-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10263628
• 关键词: Address; Anatomy; Animal Behavior; Animals; Area; Behavior; Behavioral; Behavioral Model; Biological; Biological Models; Brain; Brain region; Calcium; Cognitive; Complex; Computer Models; Computing Methodologies; Data; Data Set; Decision Making; Dimensions; Dorsal; Freedom; Goals; Image; Link; Maps; Measurement; Mental disorders; Methods; Microscopic; Modality; Modeling; Modernization; Movement; Mus; Names; Nervous system structure; Neural Network Simulation; Neurons; Neurosciences; Output; Pattern; Perception; Problem Solving; Pyramidal Cells; Research; Resolution; Structure; Systems Theory; Techniques; Testing; analytical tool; base; cell type; cognitive function; computer framework; design; dynamic system; experimental study; high dimensionality; insight; large scale data; mathematical model; multi-scale modeling; multidimensional data; neural circuit; neural patterning; neurotechnology; neurotransmission; novel; outcome prediction; predictive modeling; recurrent neural network; relating to nervous system; theories

Project Summary/Abstract A central problem in neuroscience is to understand how activity arises from neural circuits to drive animal behaviors. Solving this problem requires integrating information from multiple experimental modalities and organization levels of the nervous system. While modern neurotechnologies are generating high-resolution maps of the brain-wide neural activity and anatomical connectivity, novel theoretical frameworks are urgently needed to realize the full potential of these datasets. Most state-of-the-art methods for analyzing high-dimensional data are based on detecting correlations in neural activity and do not provide links to the underlying anatomical connectivity and circuit mechanisms. As a result, conclusions derived with these methods rarely generalize across different behaviors and are hard to validate in perturbation experiments. In contrast, mechanistic theories, which combine connectivity, activity, and function, have been highly successful in understanding function of small neural circuits. Conditions under which insights from small circuits scale to large distributed circuits have not been explored. Mechanistic theories informed by multiple data modalities are critically missing to guide experiments probing global neural dynamics on the brain-wide scale. The main goal of this proposal is to develop computational frameworks for modeling global neural dynamics, which utilize anatomical connectivity and predict rich behavioral outputs on single trials. Our project will address two complementary aims. First, we will take advantage of recently available datasets of high-resolution brain- wide neural activity and anatomical connectivity to construct a multiscale model of functional dynamics across the mouse cortex. Integrating measurements across multiple scales, from mesoscopic to near-cellular resolution, we aim to reveal the effective degrees of freedom at each scale, which constrain global neural dynamics and drive rich patterns of behavior. Second, we will leverage techniques from dynamical systems theory and artificial recurrent neural networks to develop circuit reduction methods that infer interpretable low-dimensional circuit mechanisms of cognitive computations from high-dimensional neural activity data. Rather than merely detecting correlations, our method infers the structural connectivity of an equivalent low-dimensional circuit that fits projections of high-dimensional neural activity data and implements the behavioral task. We will apply this method to multi-area neural activity recordings from behaving animals to reveal distributed circuit mechanisms of context-dependent decision making. The computational frameworks developed in this proposal can be validated in perturbation experiments and extended to other nervous systems and behaviors.

项目概要/摘要 神经科学的一个中心问题是了解神经回路的活动如何驱动动物 行为。解决这个问题需要整合来自多种实验方式的信息， 神经系统的组织层次。虽然现代神经技术正在生成高分辨率地图 关于全脑神经活动和解剖连接性，迫切需要新的理论框架 充分发挥这些数据集的潜力。用于分析高维数据的最先进的方法 基于检测神经活动的相关性，不提供与底层解剖学的链接 连接和电路机制。因此，用这些方法得出的结论很少具有概括性 跨越不同的行为，并且很难在扰动实验中验证。相比之下，机械论， 它结合了连通性、活动性和功能，在理解小型物体的功能方面取得了巨大成功。 神经回路。从小电路规模到大型分布式电路的见解还没有实现的条件 被探索过。由多种数据模式提供的机制理论严重缺乏指导 在全脑范围内探索全局神经动力学的实验。 该提案的主要目标是开发用于建模全局神经动力学的计算框架， 它利用解剖连接并在单次试验中预测丰富的行为输出。我们的项目将解决 两个互补的目标。首先，我们将利用最近可用的高分辨率大脑数据集 广泛的神经活动和解剖连接性，以构建跨领域功能动力学的多尺度模型 小鼠皮质。整合多个尺度的测量结果，从介观分辨率到近细胞分辨率， 我们的目标是揭示每个尺度的有效自由度，这限制了全局神经动力学和 驱动丰富的行为模式。其次，我们将利用动力系统理论和人工技术 循环神经网络开发电路简化方法来推断可解释的低维电路 高维神经活动数据的认知计算机制。而不仅仅是检测 相关性，我们的方法推断出适合的等效低维电路的结构连通性 高维神经活动数据的投影并实施行为任务。我们将应用这个 记录行为动物的多区域神经活动以揭示分布式电路机制的方法 依赖于上下文的决策。本提案中开发的计算框架可以是 在微扰实验中得到验证，并扩展到其他神经系统和行为。