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SyConn2: dense synaptic connectivity inference for volume electron microscopy

Philipp J. Schubert
Sven Dorkenwald
Jonathan Klimesch
Fabian Svara
Andrei Mancu
Hashir Ahmad
Michale S. Fee
Joergen Kornfeld
Nature Methods, vol. 19 (2022), 1367–1370


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.