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  Automated synaptic connectivity inference for volume electron microscopy

Dorkenwald, S., Schubert, P. J., Killinger, M. F., Urban, G., Mikula, S., Svara, F., et al. (2017). Automated synaptic connectivity inference for volume electron microscopy. Nature methods, 14(4), 435-442. doi:10.1038/nmeth.4206.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-BA13-0 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-BA14-E
Genre: Journal Article

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 Creators:
Dorkenwald, Sven1, Author              
Schubert, Philipp J.1, Author              
Killinger, Marius F.1, Author              
Urban, Gregor2, Author
Mikula, Shawn1, Author              
Svara, Fabian1, Author              
Kornfeld, Joergen1, Author              
Affiliations:
1Department: Electrons-Photons-Neurons / Denk, MPI of Neurobiology, Max Planck Society, ou_1128546              
2external, ou_persistent22              

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Free keywords: SONGBIRD BASAL GANGLIA; CEREBRAL-CORTEX; NEURAL ACTIVITY; CIRCUIT RECONSTRUCTION; DIRECTION-SELECTIVITY; WIRING SPECIFICITY; PROJECTION NEURONS; AREA-X; SEGMENTATION; RETINABiochemistry & Molecular Biology;
 Abstract: Teravoxel volume electron microscopy data sets from neural tissue can now be acquired in weeks, but data analysis requires years of manual labor. We developed the SyConn framework, which uses deep convolutional neural networks and random forest classifiers to infer a richly annotated synaptic connectivity matrix from manual neurite skeleton reconstructions by automatically identifying mitochondria, synapses and their types, axons, dendrites, spines, myelin, somata and cell types. We tested our approach on serial block-face electron microscopy data sets from zebrafish, mouse and zebra finch, and computed the synaptic wiring of songbird basal ganglia. We found that, for example, basal-ganglia cell types with high firing rates in vivo had higher densities of mitochondria and vesicles and that synapse sizes and quantities scaled systematically, depending on the innervated postsynaptic cell types.

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Language(s): eng - English
 Dates: 2017-02-272017
 Publication Status: Published in print
 Pages: 12
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: ISI: 000397900500028
DOI: 10.1038/nmeth.4206
 Degree: -

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Title: Nature methods
  Other : Nature methods
Source Genre: Journal
 Creator(s):
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Publ. Info: New York, NY : Nature Pub. Group
Pages: - Volume / Issue: 14 (4) Sequence Number: - Start / End Page: 435 - 442 Identifier: ISSN: 1548-7091
CoNE: /journals/resource/111088195279556