SyConn-Neuroglancer provides web access to connectomic datasets processed with SyConn. It is based on a fork of the original google/neuroglancer project developed and maintained by Jeremy Maitin-Shepard at Google Research in Viren Jain’s team, with contributions from many other research organisations. We aim to keep our fork in sync with the upstream project and contribute as much as possible back to the original codebase.
SyConn-Neuroglancer provides an analysis interface for straightforward connectomic queries, such as filtering neurons by properties, identifying pre- and postsynaptic partners, and listing cell types. We extend it continuously in response to analysis needs. Please contact us if you are interested in a specific analysis that would be particularly useful.
This project is developed in the Kornfeld laboratory at the MRC Laboratory of Molecular Biology (MRC LMB) in Cambridge, UK, and at the Max Planck Institute for Biological Intelligence, Martinsried, Germany. We would like to thank Eric Perlman for Neuroglancer development support.
Schubert PJ, Dorkenwald S, Januszewski M, Klimesch J, Svara F, Mancu A, Ahmad H, Fee M, Jain V, Kornfeld J. SyConn2: Dense synaptic connectivity inference for volume EM. Nat Methods. 2022. doi: 10.1038/s41592-022-01624-x.
Dorkenwald S, Schubert PJ, Killinger MF, Urban G, Mikula S, Svara F, Kornfeld J. Automated synaptic connectivity inference for volume electron microscopy. Nat Methods. 2017 Apr;14(4):435-442. doi: 10.1038/nmeth.4206. Epub 2017 Feb 27. PMID: 28250467.
Kornfeld J, Januszewski M, Schubert PJ, Jain V, Denk W, Fee M. An anatomical substrate of credit assignment in reinforcement learning. bioRxiv. 2020 Jan. doi: 10.1101/2020.02.18.954354.
Januszewski M, Kornfeld J, Li P.H. et al. High-precision automated reconstruction of neurons with flood-filling networks. Nat Methods 15, 605–610 (2018). doi: 10.1038/s41592-018-0049-4.
Schubert PJ, Dorkenwald S, Januszewski M et al. Learning cellular morphology with neural networks. Nat Commun 10, 2736 (2019). doi: 10.1038/s41467-019-10836-3.