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

项目摘要/摘要 神经科学中的一个中心问题是了解神经回路如何产生活动来驱动动物 行为。解决这个问题需要集成来自多个实验模式的信息，并 神经系统的组织水平。虽然现代神经技术正在生成高分辨率的地图 对于全脑神经活动和解剖连通性，迫切需要新的理论框架 以实现这些数据集的全部潜力。最先进的分析高维数据的方法 是基于检测神经活动中的相关性，而不是提供到基础解剖学的链接 连通性和电路机制。因此，用这些方法得出的结论很少概括。 跨越不同的行为，很难在微扰实验中验证。相比之下，机械论理论， 它们结合了连接性、活动性和功能，在理解小分子的功能方面取得了很大的成功 神经回路。从小型电路到大型分布式电路的洞察尚未实现的条件 已经被探索过了。由多种数据模式提供信息的机械论理论严重缺失以指导 在全脑范围内探索全局神经动力学的实验。 这项提议的主要目标是开发用于模拟全球神经动力学的计算框架， 它利用解剖学上的连通性，并在单次试验中预测丰富的行为输出。我们的项目将解决 两个相辅相成的目标。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，以构建跨区域功能动力学的多尺度模型 老鼠的大脑皮层。集成多个尺度的测量，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上的有效自由度，这些自由度限制了全局神经动力学和 推动丰富的行为模式。第二，我们将利用动力系统理论和人工智能的技术。 递归神经网络开发可推断可解释低维电路的电路简化方法 从高维神经活动数据进行认知计算的机制。而不是仅仅检测 关联，我们的方法推断出符合的等价低维电路的结构连通性 预测高维神经活动数据，并执行行为任务。我们将应用这一点 一种多区域记录行为动物神经活动以揭示分布式电路机制的方法 根据具体情况做出决策。在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。

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.