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SyConn2: dense synaptic connectivity inference for volume electron microscopy.

ABSTRACT: 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.

SUBMITTER: Schubert PJ

PROVIDER: S-EPMC9636020 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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SyConn2: dense synaptic connectivity inference for volume electron microscopy.

Schubert Philipp J PJ Dorkenwald Sven S Januszewski Michał M Klimesch Jonathan J Svara Fabian F Mancu Andrei A Ahmad Hashir H Fee Michale S MS Jain Viren V Kornfeld Joergen J

Nature methods 20221024 11

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 am ...[more]

PMID: 36280715

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