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Update about and data page templates
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‎python/neuroglancer/templates/about.html‎

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<a class="nav-link" aria-current="page" href="index.html">Home</a>
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<a class="nav-link" href="tutorials.html">Tutorials</a>
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<a class="nav-link" href="data.html">Data</a>
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<a class="nav-link" href="interactive/index.html">SyConn interactive</a>
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<a class="nav-link active" href="about.html">About</a>
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</div>
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</div>
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<div class="container m-3">
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<h3 class="display-6 header">Project</h3>
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<p class="lead" style="text-align: justify;">SyConn-Neuroglancer provides web access to connectomic datasets processed with SyConn. It is based on a fork of the original <a href="https://github.com/google/neuroglancer">google/neuroglancer</a> project developed and maintained by Jeremy Maitin-Shepard at Google Research in Viren Jain’s team, with contributions from many other research organizations. We aim to keep our fork in sync with the upstream project and contribute as much as possible back to the original code base.</p>
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<p class="lead" style="text-align: justify;">SyConn web provides an analysis interface for simple connectomic queries, such as filtering of the neurons based on properties, the identification of pre- and postsynaptic neurons or the listing of different cell types and is continuously extended by us based on actual analysis needs. Please write to us if you are interested in a specific analysis that might be particularly useful.</p>
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{# <h3 class="display-6 header" id="dataset">Dataset</h3>#}
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{# <h4>j0251</h4>#}
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{# <p class="lead" style="text-align: justify;">#}
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{# EM data acquisition: <i>J. Kornfeld</i> <br>#}
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{# Alignment: <i>J. Kornfeld & A. Pope (V. Jain's team, Google Research)</i> <br>#}
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{# SyConn processing: <i>P. Schubert</i> <br>#}
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{# Cell & ultrastructure segmentation: <i>M. Januszewski (V. Jain's team, Google Research)</i>#}
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{# </p>#}
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{# <h4>j0126</h4>#}
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{# <p class="lead" style="text-align: justify;">#}
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{# EM data acquisition: <i>J. Kornfeld</i> <br>#}
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{# Alignment: <i>J. Kornfeld & A. Pope (V. Jain's team, Google Research)</i> <br>#}
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{# SyConn processing: <i>P. Schubert</i> <br>#}
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{# Cell & ultrastructure segmentation: <i>M. Januszewski (V. Jain's team, Google Research), S. Dorkenwald, P. Schubert</i>#}
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{# </p>#}
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{# <p class="lead" style="text-align: justify;">#}
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{# Please note that the neuron reconstructions, synapse predictions and cell type classifications are best effort. We would appreciate feedback if you think you have discovered a systematic problem that deserves more attention! We are also currently working on creating a downloadable version of the data for offline analysis.#}
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{# </p>#}
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<h3 class="display-6 header">The Team</h3>
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<p class="lead" style="text-align: justify;">This project is developed in the Kornfeld laboratory at the Max Planck Institute for Biological Intelligence (in foundation) in Martinsried, Germany. We would like to thank Eric Perlman for neuroglancer development support.
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<p class="lead" style="text-align: justify;">SyConn-Neuroglancer provides web access to connectomic datasets processed with SyConn. It is based on a fork of the original <a href="https://github.com/google/neuroglancer">google/neuroglancer</a> 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.</p>
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<p class="lead" style="text-align: justify;">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.</p>
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<p class="lead" style="text-align: justify;">This project is developed in the <a href="https://kornfeldlab.org" class="text-primary">Kornfeld laboratory</a> 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.</p>
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<p></p>
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<h3 class="display-6 header">Alumni</h3>
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<div class="row">
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<div class="col-sm-3">
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<div class="card">
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<div class="card-body">
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<h5 class="card-title">Hashir Ahmad</h5>
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<h6 class="card-subtitle text-muted">Maintainer</h6>
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<p class="card-text"></p>
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<a href="mailto:hashir.ahmad@bi.mpg.de" class="btn btn-primary">Contact</a>
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</div>
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</div>
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<div class="card"><div class="card-body"><h5 class="card-title">Hashir Ahmad</h5></div></div>
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</div>
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<div class="col-sm-3">
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<div class="card">
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<div class="card-body">
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<h5 class="card-title">Andrei Mancu</h5>
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<h6 class="card-subtitle text-muted">Developer</h6>
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<p class="card-text"></p>
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<a href="mailto:andrei.mancu@bi.mpg.de" class="btn btn-primary">Contact</a>
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</div>
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</div>
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<div class="card"><div class="card-body"><h5 class="card-title">Andrei Mancu</h5></div></div>
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<div class="col-sm-3">
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<div class="card">
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<div class="card-body">
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<h5 class="card-title">Alexandra Rother</h5>
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<h6 class="card-subtitle text-muted">Developer</h6>
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<p class="card-text"></p>
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<a href="mailto:alexandra.rother@bi.mpg.de" class="btn btn-primary">Contact</a>
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</div>
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</div>
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<div class="card"><div class="card-body"><h5 class="card-title">Ana-Maria Lacatusu</h5></div></div>
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<div class="col-sm-3">
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<div class="card">
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<div class="card-body">
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<h5 class="card-title">Ana-Maria Lacatusu</h5>
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<h6 class="card-subtitle text-muted">Developer</h6>
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<p class="card-text"></p>
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<a href="mailto:anamaria.lacatusu@bi.mpg.de" class="btn btn-primary">Contact</a>
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</div>
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</div>
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<div class="card"><div class="card-body"><h5 class="card-title">Philipp Schubert</h5></div></div>
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<div class="col-sm-3">
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<div class="card">
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<h5 class="card-title">Philipp Schubert</h5>
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<h6 class="card-subtitle text-muted">Guest Scientist</h6>
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<p class="card-text"></p>
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<a href="mailto:pschubert@neuro.mpg.de" class="btn btn-primary">Contact</a>
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</div>
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</div>
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<div class="col-sm-3">
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<div class="card">
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<h5 class="card-title">Jörgen Kornfeld</h5>
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<h6 class="card-subtitle text-muted">PI</h6>
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<p class="card-text"></p>
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<a href="mailto:joergen.kornfeld@bi.mpg.de" class="btn btn-primary">Contact</a>
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</div>
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</div>
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<div class="card"><div class="card-body"><h5 class="card-title">Sven Dorkenwald</h5></div></div>
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<p></p>
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<h3 class="display-6 header" id="publications">Publications</h3>
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<p class="lead" style="text-align: justify;">
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Schubert PJ, Dorkenwald S, Januszewski M, Klimesch J, Svara F, Mancu A, Ahmad H, Fee M, Jain V, Kornfeld J. <cite>SyConn2: Dense synaptic connectivity inference for volume EM.</cite> Nat Methods. 2022, in the press.
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Schubert PJ, Dorkenwald S, Januszewski M, Klimesch J, Svara F, Mancu A, Ahmad H, Fee M, Jain V, Kornfeld J. <cite>SyConn2: Dense synaptic connectivity inference for volume EM.</cite> Nat Methods. 2022. doi: <a href="https://www.nature.com/articles/s41592-022-01624-x">10.1038/s41592-022-01624-x</a>.
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<p class="lead" style="text-align: justify;">
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Dorkenwald S, Schubert PJ, Killinger MF, Urban G, Mikula S, Svara F, Kornfeld J. <cite>Automated synaptic connectivity inference for volume electron microscopy.</cite> Nat Methods. 2017 Apr;14(4):435-442. doi:<a href="https://www.nature.com/articles/nmeth.4206">10.1038/nmeth.4206.</a> Epub 2017 Feb 27. PMID: 28250467.
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Dorkenwald S, Schubert PJ, Killinger MF, Urban G, Mikula S, Svara F, Kornfeld J. <cite>Automated synaptic connectivity inference for volume electron microscopy.</cite> Nat Methods. 2017 Apr;14(4):435-442. doi: <a href="https://www.nature.com/articles/nmeth.4206">10.1038/nmeth.4206</a>. Epub 2017 Feb 27. PMID: 28250467.
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<p class="lead" style="text-align: justify;">
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Kornfeld J, Januszewski M, Schubert PJ, Jain V, Denk W, Fee M. <cite>An anatomical substrate of credit assignment in reinforcement learning.</cite> bioRxiv. 2020 Jan. doi:<a href="https://doi.org/10.1101/2020.02.18.954354">10.1101/2020.02.18.954354</a>
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Kornfeld J, Januszewski M, Schubert PJ, Jain V, Denk W, Fee M. <cite>An anatomical substrate of credit assignment in reinforcement learning.</cite> bioRxiv. 2020 Jan. doi: <a href="https://doi.org/10.1101/2020.02.18.954354">10.1101/2020.02.18.954354</a>.
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<p class="lead" style="text-align: justify;">
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Januszewski M, Kornfeld J, Li P.H. et al. <cite>High-precision automated reconstruction of neurons with flood-filling networks.</cite> Nat Methods 15, 605–610 (2018). doi:<a href="https://doi.org/10.1038/s41592-018-0049-4">10.1038/s41592-018-0049-4</a>
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Januszewski M, Kornfeld J, Li P.H. et al. <cite>High-precision automated reconstruction of neurons with flood-filling networks.</cite> Nat Methods 15, 605–610 (2018). doi: <a href="https://doi.org/10.1038/s41592-018-0049-4">10.1038/s41592-018-0049-4</a>.
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<p class="lead" style="...">
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Schubert PJ, Dorkenwald S, Januszewski M et al. <cite>Learning cellular morphology with neural networks.</cite> Nat Commun 10, 2736 (2019). doi: <a href="https://doi.org/10.1038/s41467-019-10836-3">10.1038/s41467-019-10836-3</a>
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<p class="lead" style="text-align: justify;">
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Schubert PJ, Dorkenwald S, Januszewski M et al. <cite>Learning cellular morphology with neural networks.</cite> Nat Commun 10, 2736 (2019). doi: <a href="https://doi.org/10.1038/s41467-019-10836-3">10.1038/s41467-019-10836-3</a>.
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</body>

‎python/neuroglancer/templates/data.html‎

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<a class="nav-link" aria-current="page" href="index.html">Home</a>
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<a class="nav-link" href="tutorials.html">Tutorials</a>
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<a class="nav-link active" href="data.html">Data</a>
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<a class="nav-link" href="interactive/index.html">SyConn interactive</a>
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<a class="nav-link" href="about.html">About</a>
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<div>
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<div class="container m-3">
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<h3 class="display-6 header" id="dataset">Data</h3>
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<h4>j0251 - adult zebra finch area X</h4>
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<h4>j0251 – adult zebra finch area X</h4>
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<p class="lead" style="text-align: justify;">
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EM data acquisition: <i>J. Kornfeld</i> <br>
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Alignment: <i>J. Kornfeld & A. Pope (V. Jain's team, Google Research)</i> <br>
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SyConn processing: <i>P. Schubert</i> <br>
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SyConn processing: <i>P. Schubert, A. Rother, H. Ahmad, J. Kornfeld</i> <br>
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Cell & ultrastructure segmentation: <i>M. Januszewski (V. Jain's team, Google Research)</i>
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<h4>j0126 - adult zebra finch area X</h4>
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<h4>j0126 – adult zebra finch area X</h4>
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EM data acquisition: <i>J. Kornfeld</i> <br>
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Alignment: <i>J. Kornfeld & A. Pope (V. Jain's team, Google Research)</i> <br>
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SyConn processing: <i>P. Schubert</i> <br>
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SyConn processing: <i>P. Schubert, J. Kornfeld, S. Dorkenwald</i> <br>
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Cell & ultrastructure segmentation: <i>M. Januszewski (V. Jain's team, Google Research), S. Dorkenwald, P. Schubert</i>
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<p class="lead" style="text-align: justify;">
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Please note that the neuron reconstructions, synapse predictions and cell type classifications are best effort. We would appreciate feedback if you think you have discovered a systematic problem that deserves more attention! We are also currently working on creating a downloadable version of the data for offline analysis. If you are interested in running the SyConn analysis steps (
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cf. <a href="https://www.nature.com/articles/s41592-022-01624-x">SyConn2 paper</a>
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), you can find the example data and models
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<a href="https://datashare.mpcdf.mpg.de/s/OuP4ST7AbNo8qYE">here</a>.
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Please note that neuron reconstructions, synapse predictions, and cell-type classifications are best‑effort. We welcome feedback if you think you have discovered a systematic issue that deserves more attention. We are also working on a downloadable version of the data for offline analysis.
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<p class="lead" style="text-align: justify;">
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The currently available data sets, j0251 (256 x 256 x 384 µm) and j0126 (96 x 98 x 114 µm), were acquired from adult male zebra finch area X, a basal ganglia area responsible for song learning that contains striatal and pallidal-like cell types. Both data sets were acquired in the department of Winfried Denk at the MPI for Medical Research, Heidelberg.
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The currently available datasets, j0251 (256 × 256 × 384 µm) and j0126 (96 × 98 × 114 µm), were acquired from adult male zebra finch area X, a basal ganglia area responsible for song learning that contains striatal- and pallidal‑like cell types. Both datasets were acquired in the department of Winfried Denk at the MPI for Medical Research, Heidelberg.
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<hr class="border border-dark rounded border-1 mx-3">
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<div class="container m-3">
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<h3 class="header display-6">Connectome analysis</h3>
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<p class="lead" style="text-align: justify;">
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For interactive analyses of the connectome, use SyConn interactive in the browser. See the
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<a href="https://syconn.esc.mpcdf.mpg.de/interactive/" target="_blank">API notebook</a> for examples and guidance.
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</p>
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</div>
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<!-- <div class="row"> -->
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<div class="container m-3">
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<div class="col-12 col-md-8 col-lg-6 text-md-start text-center mb-3">
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<h3 class="header display-6">Get Started</h3>
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<h3 class="header display-6">Connectome viewer</h3>
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<form action="/_generate_token" method="post">
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