Journal
CELL
Volume 162, Issue 3, Pages 648-661Publisher
CELL PRESS
DOI: 10.1016/j.cell.2015.06.054
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Funding
- NIH/NINDS [1DP2OD006514-01, TR01 1R01NS076467-01, 1U01NS090449-01]
- Conte [1P50MH09427101]
- MURI Army Research Office [W911NF1210594, IIS-1447786]
- NSF [OIA-1125087, IIS-1110955]
- DARPA [HR0011-14-2-0004]
- Human Frontier Science Program [RGP0051/2014]
- JHU Applied Physics Laboratory
- Research Program for Applied Neuroscience
- Howard Hughes Medical Institute
- Nvidia
- Intel
- CRCNS [1R01EB016411]
- U.S. Department of Defense (DOD) [W911NF1210594] Funding Source: U.S. Department of Defense (DOD)
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1447786] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1447344] Funding Source: National Science Foundation
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We describe automated technologies to probe the structure of neural tissue at nanometer resolution and use them to generate a saturated reconstruction of a sub-volume of mouse neocortex in which all cellular objects (axons, dendrites, and glia) and many sub-cellular components (synapses, synaptic vesicles, spines, spine apparati, postsynaptic densities, and mitochondria) are rendered and itemized in a database. We explore these data to study physical properties of brain tissue. For example, by tracing the trajectories of all excitatory axons and noting their juxtapositions, both synaptic and nonsynaptic, with every dendritic spine we refute the idea that physical proximity is sufficient to predict synaptic connectivity (the so-called Peters' rule). This online minable database provides general access to the intrinsic complexity of the neocortex and enables further data-driven inquiries.
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