| dc.contributor.author | Schubert, Philipp J | |
| dc.contributor.author | Dorkenwald, Sven | |
| dc.contributor.author | Januszewski, Michał | |
| dc.contributor.author | Klimesch, Jonathan | |
| dc.contributor.author | Svara, Fabian | |
| dc.contributor.author | Mancu, Andrei | |
| dc.contributor.author | Ahmad, Hashir | |
| dc.contributor.author | Fee, Michale S | |
| dc.contributor.author | Jain, Viren | |
| dc.contributor.author | Kornfeld, Joergen | |
| dc.date.accessioned | 2023-03-27T13:46:25Z | |
| dc.date.available | 2023-03-27T13:46:25Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/148775 | |
| dc.description.abstract | <jats:title>Abstract</jats:title><jats:p>The 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.</jats:p> | en_US |
| dc.language.iso | en | |
| dc.publisher | Springer Science and Business Media LLC | en_US |
| dc.relation.isversionof | 10.1038/S41592-022-01624-X | en_US |
| dc.rights | Creative Commons Attribution 4.0 International license | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.source | Nature | en_US |
| dc.title | SyConn2: dense synaptic connectivity inference for volume electron microscopy | en_US |
| dc.type | Article | en_US |
| dc.identifier.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). | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences | en_US |
| dc.relation.journal | Nature Methods | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2023-03-27T13:41:24Z | |
| dspace.orderedauthors | Schubert, PJ; Dorkenwald, S; Januszewski, M; Klimesch, J; Svara, F; Mancu, A; Ahmad, H; Fee, MS; Jain, V; Kornfeld, J | en_US |
| dspace.date.submission | 2023-03-27T13:41:27Z | |
| mit.journal.volume | 19 | en_US |
| mit.journal.issue | 11 | en_US |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
