Influence of internal state on communication in distributed neuronal circuits

摘要/摘要，项目1 我们对周围世界的反应是由我们复杂的内部状态的各个方面控制的。为 例如，当我们更加警惕和参与时，我们更有可能采取行动，我们更有可能 当我们对环境有预先的预期时，我们会对我们的感知做出特殊的解释。 了解这种内部状态变化的神经基础对于解开基本机制是很重要的 哺乳动物灵活行为的研究，以及理解自闭症等状态障碍的病因。这里 我们建议研究两种类型的内部状态变化的神经机制：自发的 参与度的波动和感知偏差的目标导向变化。该团队是国际 大脑实验室，一个已经建立的联盟，已经开发了一个标准化的小鼠决策任务 和标准化的训练、神经测量和数据分析方法，以及沿着的有效的、可扩展的 共享数据的基础设施。我们将测试一个新的假设，即这些州之间的行为差异 是由大脑区域之间信息流结构的改变引起的。具体来说，我们假设 脱离任务会抑制特定规模的人口活动向下游的传播， 结构，而变化的偏见，有利于一个选择另一个旋转的信息维度 在大脑中传播。为了研究这些假设，我们将利用我们最近的发展 大规模记录神经活动的技术和量化行为状态的算法， 大脑区域之间的多维交流模式。我们将首先同时记录大量， 使用Neuropixels 2.0探针和系统地从关键的大脑区域集中的密集神经元群体 描述这些区域之间相关性的维度和幅度。然后，我们将量化如何 这些相关模式依赖于内部状态，使用自发状态的新算法量化 在已经建立的标准化和高通量行为任务期间的转换， 国际大脑实验室最后，我们将开发和应用一类新的分析方法， 测量三个或更多同时记录的大脑区域之间的相互作用，以确定 一个区域选通或调制其他区域之间的多维通信，从而发现 控制信息流的假定控制器区域。该项目将提供第一个系统的 皮层和皮层下区域的多维通信模式的表征; 关于大脑中信息路由的新假设;量化大数据之间关系的算法 神经元群体;以及一个大规模的开放共享的神经活动数据集， 老鼠的大脑

Summary/Abstract, Project 1 Our responses to the world around us are controlled by diverse aspects of our complex internal states. For example, we are more likely to take action when we are more vigilant and engaged, and we are more likely to give particular interpretations to our percepts when we have prior expectations about our environment. Understanding the neural basis of such internal state changes is important for unraveling the basic mechanisms of flexible behavior in mammals and for understanding the etiology of disorders of state such as autism. Here we propose to investigate the neural mechanisms underlying two types of internal state changes: spontaneous fluctuations in engagement and goal-directed changes in perceptual bias. The team is part of the International Brain Laboratory, an established consortium that has developed a standardized mouse decision-making task and standardized methods for training, neural measurement, and data analysis, along with a working, scalable infrastructure for sharing data. We will test the novel hypothesis that behavioral differences across these states result from alterations in the structure of information flow between brain regions. Specifically, we hypothesize that disengaging from a task dampens propagation of specific dimensions of population activity to downstream structures, and that changing bias to favor one choice over another rotates the dimensions of information propagation across the brain. To investigate these hypotheses, we will take advantage of our recent development of technology for recording neural activity at large scale and of algorithms that quantify behavioral states and multi-dimensional communication patterns between brain regions. We will first simultaneously record large, dense populations of neurons from key sets of brain regions using Neuropixels 2.0 probes and systematically characterize the dimensionality and magnitude of correlations between these regions. Then, we will quantify how these correlation patterns depend on internal state, using novel algorithmic quantification of spontaneous state transitions during the standardized and high-throughput behavioral task that has already been established by the International Brain Laboratory. Finally, we will develop and apply a new class of analysis methods designed to measure the interactions between three or more simultaneously recorded brain regions to identify whether one region gates or modulates the multi-dimensional communication between the other regions, thus discovering putative controller regions that direct the flow of information. This project will deliver the first systematic characterization of multi-dimensional communication patterns across cortical and subcortical regions; tests of new hypotheses about information routing in the brain; algorithms that quantify the relationships between large populations of neurons; and a large-scale openly shared dataset of neural activity during flexible behavior across the mouse brain.