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

项目总结/摘要 神经科学的一个中心问题是了解神经回路如何产生活动，以驱动动物 行为。解决这个问题需要整合来自多种实验模式的信息， 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地图， 全脑神经活动和解剖连接的新的理论框架是迫切需要的 来实现这些数据集的全部潜力。分析高维数据的最先进方法 基于检测神经活动的相关性，并且不提供与基础解剖结构的链接。 连接和电路机制。因此，用这些方法得出的结论很少具有普遍性 跨越不同的行为，并且很难在扰动实验中验证。相反，机械论， 结合了联合收割机的连通性、活动性和功能性，在理解小细胞的功能方面非常成功。 神经回路从小型电路规模到大型分布式电路的洞察力尚未达到的条件 被探索。由多种数据模式提供信息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， 它利用解剖学上的连接性，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，以构建跨神经元的功能动力学的多尺度模型。 老鼠的大脑皮层整合多个尺度的测量，从介观到近细胞分辨率， 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， 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智能技术， 递归神经网络，用于开发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，我们的方法推断出一个等效的低维电路，适合结构连接 预测的高维神经活动数据和实现的行为任务。我们将应用这个 从行为动物的多区域神经活动记录揭示分布式电路机制的方法 情境相关的决策。在这个提议中开发的计算框架可以是 在扰动实验中得到验证，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.