SyConn2: dense synaptic connectivity inference for volume electron microscopy
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<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>
Date issued
2022Department
Massachusetts Institute of Technology. Department of Brain and Cognitive SciencesJournal
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