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.
Topics & Concepts
Electron microscopeInferenceVolume (thermodynamics)MicroscopyComputational biologyBiophysicsComputer scienceNeuroscienceStatistical physicsBiologyPhysicsArtificial intelligenceOpticsQuantum mechanicsAdvanced Electron Microscopy Techniques and ApplicationsAdvanced Memory and Neural ComputingNeural dynamics and brain function