Anatomical connectivity and activity in primary visual cortex of mouse

项目摘要 据估计，人类大脑中轴突“连接”的总长度约为数十万。 几公里的距离。理解细胞之间连接的基本原理是一件令人望而生畏的事情- ING任务，但越来越清楚的是，各层之间存在规范的连接模式 哺乳动物大脑皮层的。其中许多像元之间的成对连通性规则是使用 切片中的多补丁，但检查更高阶的连通性主题(例如，三角形主题)是不同的fi崇拜 在切片准备过程中。此外，神经科学的一个中心解释目标是将功能适当-- 神经元与它们之间潜在的连通性的联系。实现这一目标需要克服SignifiCan 技术挑战，但一些英勇的研究已经设法确定了这样的功能/结构原则，如 更喜欢类似定向刺激的视觉皮质细胞中增强的“相似”连接性。在过去的时间里 在过去的几年里，我们的团队参与了一个“登月”项目，该项目是IARPA和Brain Initiative资助的fi 微米项目从一毫米级的老鼠视觉立方体中收集功能和突触规模的解剖数据 大脑皮层。这一卷的功能性体内钙成像是在豪斯的贝勒医学院进行的- 吨，然后老鼠被运往西雅图，在那里同样体积的提取，准备，切片在40纳米 厚度，并在一系列先进的电子显微镜上成像。最后，大约2PB 图像堆叠是由普林斯顿大学的塞巴斯蒂安·盛的团队对齐并分割的。实现这一目标- fi的目标几乎花了微米计划的整个Ve年，该计划于2021年7月结束。此数据集 现在已经与整个神经科学界共享，并在Sciencefic++方面具有巨大的未开发潜力。 发现号。在目标1中，我们将使用图论方法，重点分析识别局部高阶 跨层的电路主题和跨皮层的兴奋性神经元之间的大规模模块聚焦 在小鼠V1中。我们将检验这一假设，即一组兴奋性神经元形成紧密联系的模块 与其他模块的稀疏、相互连接。在目标2中，我们将重点放在将结构与功能联系起来。在 在本地电路级别，我们将描述刺激选择性和内部连接性之间的关系 横跨V1的皮质层。我们将检验这样一个假设，即连接的神经元组(即结构模块) 形成计算模块以表示类似的刺激偏好(如纹理)。对于这些分析，我们 是否会利用经过验证的深度学习预测模型，提供fl灵活、系统的方法来表征 甚至非经典的、非线性的神经元和fi的特征选择性和神经元最兴奋的输入。

Project Summary Estimates of the total length of axonal "wiring" in the human brain are on the order of hundreds of thousands of kilometers. Understanding the fundamental principles underlying the connectivity between cells is a daunt- ing task, but it has become increasingly clear that there are canonical connectivity patterns across the layers of the mammalian cortex. Many of these pairwise connectivity rules between cells have been discovered using multi-patching in slices, but examining higher-order connectivity motifs (for example, triangular motifs) is difficult in the slice preparation. Furthermore, a central explanatory goal of neuroscience is to relate functional proper- ties of neurons to the underlying connectivity between them. Achieving this goal requires overcoming significant technical challenges, but a few heroic studies have managed to identify such functional/structural principles such as enhanced "like-to-like" connectivity in visual cortex cells that prefer similarly oriented stimuli. Over the past five years, our team has participated in a "moon-shot" project as part of the IARPA and BRAIN Initiative-funded MICrONS project to collect functional and synaptic-scale anatomical data from a millimeter cube of mouse visual cortex. Functional in vivo calcium imaging of this volume was performed at Baylor College of Medicine in Hous- ton, then the mouse was shipped to Seattle where the same volume was extracted, prepared, sliced at 40nm thickness, and imaged on an array of advanced electron microscopes. Finally, the approximately two petabyte image stack was finely-aligned and segmented by Sebastian Seung's group at Princeton. Achieving this ambi- tious goal took almost the entire five years of the MICrONS program which ended in July 2021. This data set has now beeen shared with the entire neuroscience community and has huge untapped potential for scientific discovery. In Aim 1 we will use graph theoretical methods and focus our analysis to identify local higher-order circuit motifs across layers and large-scale modules between excitatory neurons across cortical layers focusing in mouse V1. We will test the hypothesis that groups of excitatory neurons form tightly-connected modules with sparse, reciprocal connections to other modules. In Aim 2 we will focus on relating structure to function. At the local circuit level we will characterize the relationships between stimulus selectivity and connectivity within and across cortical layers in V1. We will test the hypothesis that connected groups of neurons (i.e. structural modules) form computational modules to represent similar stimulus preferences (such as textures). For these analyses we will leverage validated deep learning predictive models that provide a flexible, systematic method to characterize even non-classical, non-linear feature selectivities of neurons and find the neuron's most-exciting inputs.