SyConn2: dense synaptic connectivity inference for volume electron microscopy
Name
s41592-022-01624-x.pdf
Description
Published version
Size
2.49 MB
Format
Adobe PDF
Checksum (MD5)
b2911c50c2e03efbb43530428e635176
Author(s)
Schubert, Philipp J
Dorkenwald, Sven
Januszewski, Michał
Klimesch, Jonathan
Svara, Fabian
Mancu, Andrei
Ahmad, Hashir
Fee, Michale S
Jain, Viren
Kornfeld, Joergen
Date Issued
2022
Journal
Nature Methods
Publisher
Springer Science and Business Media LLC
Citation
Schubert, Philipp J, Dorkenwald, Sven, Januszewski, Michał, Klimesch, Jonathan, Svara, Fabian et al. 2022. "SyConn2: dense synaptic connectivity inference for volume electron microscopy." Nature Methods, 19 (11).
Version
Final published version
Abstract
AbstractThe ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41592-022-01624-X
