If you use parts of this code base in your academic projects, please cite the corresponding publication.
Documentation
--------------
To get started, please have a look at our [documentation](https://structuralneurobiologylab.github.io/SyConn/documentation/), but be aware that it is currently outdated and applies only to SyConn v1. We also present more general information about SyConn on our [Website](https://structuralneurobiologylab.github.io/SyConn/).
Documentation
-------------
The documentation for the refactored version is still work-in-progress and can be found [here](docs/doc.md). Alternatively see the latest [readthedocs build](https://syconn.readthedocs.io/en/latest/).
For SyConn v1, please have a look at the old [documentation](https://structuralneurobiologylab.github.io/SyConn/documentation/). We also present more general information about SyConn on our [Website](https://structuralneurobiologylab.github.io/SyConn/).
# The Team
The Team
--------
The Synaptic connectivity inference toolkit is developed at the Max-Planck-Institute of Neurobiology in Martinsried by
Philipp Schubert, Maria Kawula, Carl Constantin v. Wedemeyer, Atul Mohite, Gaurav Kumar and Joergen Kornfeld.
# Acknowledgements
We thank deepmind for providing egl extension code to handle multi-gpu rendering on the same machine, which is under the Apache License 2.0. The original code snippet used in our
project can be found [here](https://github.com/deepmind/dm_control/blob/30069ac11b60ee71acbd9159547d0bc334d63281/dm_control/_render/pyopengl/egl_ext.py).
Acknowledgements
----------------
We are especially grateful for the support by Winfried Denk who enabled this work in his department. We also want to thank Christian Guggenberger
and his group at the MPCDF for cluster support and deepmind for providing egl extension code to handle multi-gpu rendering on the same machine.
The original code snippet (under the Apache License 2.0) used for our project can be found
\[1\][Automated synaptic connectivity inference for volume electron microscopy][https://www.nature.com/articles/nmeth.4206]
References
----------
\[1\][Automated synaptic connectivity inference for volume electron microscopy](https://www.nature.com/articles/nmeth.4206)
```
@ARTICLE{SyConn2017,
title = "Automated synaptic connectivity inference for volume electron
...
...
@@ -53,7 +59,7 @@ We thank deepmind for providing egl extension code to handle multi-gpu rendering
```
\[2\][Learning cellular morphology with neural networks][https://doi.org/10.1038/s41467-019-10836-3]
\[2\][Learning cellular morphology with neural networks](https://doi.org/10.1038/s41467-019-10836-3)
```
@Article{Schubert2019,
author={Schubert, Philipp J.
...
...
@@ -67,7 +73,15 @@ year={2019},
volume={10},
number={1},
pages={2736},
abstract={Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction as well as automated detection of synapses. However, methods for automating the morphological analysis of nanometer-resolution reconstructions are less established, despite the diversity of possible applications. Here, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training, we infer morphology embeddings (Neuron2vec) of neuron reconstructions and train CMNs to identify glia cells in a supervised classification paradigm, which are then used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify subcellular compartments and the cell types of neuron reconstructions.},
abstract={Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but
can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated
neuron reconstruction as well as automated detection of synapses. However, methods for automating the morphological
analysis of nanometer-resolution reconstructions are less established, despite the diversity of possible applications.
Here, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically
reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training, we infer morphology embeddings
(Neuron2vec) of neuron reconstructions and train CMNs to identify glia cells in a supervised classification paradigm,
which are then used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify
subcellular compartments and the cell types of neuron reconstructions.},
