SyConn2: dense synaptic connectivity inference for volume electron microscopy

Philipp J. Schubert, Sven Dorkenwald, Michał Januszewski, Jonathan Klimesch, Fabian Svara, Andrei Mancu, Hashir Ahmad, Michale S. Fee, Viren Jain, Joergen Kornfeld

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. SyConn2 is a machine learning-based framework for inferring and analyzing the connectomes contained in a volume electron microscopy dataset of brain tissue, for example from the zebra finch.

External IDs:doi:10.1038/s41592-022-01624-x