连接组学
神经科学
视皮层
连接体
计算机科学
钙显像
电池类型
功能连接
生物
细胞
化学
遗传学
有机化学
钙
作者
J. Alexander Bae,Mahaly Baptiste,Caitlyn Bishop,Ágnes L. Bodor,Derrick Brittain,JoAnn Buchanan,Daniel J. Bumbarger,Manuel Castro,Brendan Celii,Erick Cobos,Forrest Collman,Nuno Maçarico da Costa,Sven Dorkenwald,Leila Elabbady,Paul G. Fahey,Tim Fliss,Emmanouil Froudarakis,Jay Gager,Clare Gamlin,William Gray-Roncal
标识
DOI:10.1101/2021.07.28.454025
摘要
Abstract To understand the brain we must relate neurons’ functional responses to the circuit architecture that shapes them. Here, we present a large functional connectomics dataset with dense calcium imaging of a millimeter scale volume. We recorded activity from approximately 75,000 neurons in primary visual cortex (VISp) and three higher visual areas (VISrl, VISal and VISlm) in an awake mouse viewing natural movies and synthetic stimuli. The functional data were co-registered with a volumetric electron microscopy (EM) reconstruction containing more than 200,000 cells and 0.5 billion synapses. Subsequent proofreading of a subset of neurons in this volume yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Here, we release this dataset as an open-access resource to the scientific community including a set of tools that facilitate data retrieval and downstream analysis. In accompanying papers we describe our findings using the dataset to provide a comprehensive structural characterization of cortical cell types 1–3 and the most detailed synaptic level connectivity diagram of a cortical column to date 2 , uncovering unique cell-type specific inhibitory motifs that can be linked to gene expression data 4 . Functionally, we identify new computational principles of how information is integrated across visual space 5 , characterize novel types of neuronal invariances 6 and bring structure and function together to decipher a general principle that wires excitatory neurons within and across areas 7, 8 .
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