Synaptic connectivity inference toolkit developed at Max-Planck-Institute of Neurobiology, Munich <br/>
Authors: Philipp Schubert, Sven Dorkenwald, Joergen Kornfeld <br/>
Refactored (still an early stage construction) version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data. For the first version
see 'SyConn' below. Current features:
Refactored (still an early stage construction) version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data.
For the first version see branch 'dorkenwald2017nm'.
Version 2 currently features:
- introduction of supervoxel and agglomerated supervoxel classes
- added support for (sub-) cellular compartment (spines, axon/dendrite/soma) and cell type classification with [skeleton](https://www.nature.com/articles/nmeth.4206)- and [multiview-based](https://www.biorxiv.org/content/early/2018/07/06/364034) approaches
- cell organelle prediction, extraction and mesh generation
- glia identification and splitting
-[glia identification and splitting](https://www.biorxiv.org/content/early/2018/07/06/364034)
- generation of connectivity matrix
Documentation
--------------
_in progress_
# SyConn
Synaptic connectivity inference toolkit developed at the Max-Planck-Institute for Medical Research, Heidelberg and
Max-Planck-Institute of Neurobiology, Munich <br/>
Authors: Sven Dorkenwald, Philipp Schubert, Joergen Kornfeld <br/>
Documentation
--------------
To get started, please have a look at our [documentation](https://structuralneurobiologylab.github.io/SyConn/documentation/), with information on how to run our [example](https://github.com/StructuralNeurobiologyLab/SyConn/blob/master/examples/full_run_example.py). We also present more general information about SyConn on our [Website](https://structuralneurobiologylab.github.io/SyConn/).
Publication
-----------
The first version of SyConn (see branch 'dorkenwald2017nm') was published in [Nature Methods](http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.4206.html) on February 27th 2017. If you use parts of this code base in your academic projects, please cite the corresponding publication. <br/>
```
@ARTICLE{SyConn2017,
title = "Automated synaptic connectivity inference for volume electron
microscopy",
author = "Dorkenwald, Sven and Schubert, Philipp J and Killinger, Marius F
and Urban, Gregor and Mikula, Shawn and Svara, Fabian and
Kornfeld, Joergen",
abstract = "SyConn is a computational framework that infers the synaptic
wiring of neurons in volume electron microscopy data sets with
machine learning. It has been applied to zebra finch, mouse and
zebrafish neuronal tissue samples.",
journal = "Nat. Methods",
publisher = "Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.",
year = 2017,
month = Feb,
day = 27,
url = http://dx.doi.org/10.1038/nmeth.4206
}
```
## System Requirements & Installation
* Python 3.5
* The whole pipeline was designed and tested on Linux systems (CentOS, Arch)
* SyConn is based on the packages `elektronn <http://elektronn.org>`_, `knossos-utils <https://github.com/knossos-project/knossos_utils>`_
* `KNOSSOS <http://knossostool.org/>`_ is used for visualization and annotation of 3D EM data sets.
* [VIGRA](https://ukoethe.github.io/vigra/), e.g. ``conda install -c ukoethe vigra``
This page gives a brief example for a full run of then main components of SyConn.
The corresponding code is available in ``/examples/full_run_example.py`` and
the example data with all pre-trained models can be found in ``SyConnDenseCube(.zip)`` (go to our `Website <http://structuralneurobiologylab.github.io/SyConn/>`_ for Download).
* The whole pipeline was designed and tested on Linux systems (CentOS, Arch)
* SyConn is based on the packages `ELEKTRONN <http://elektronn.org>`_, `Knossos Python-Tools <https://github.com/knossos-project/knossos_python_tools>`_
* SyConn is based on the packages `elektronn <http://elektronn.org>`_, `knossos-utils <https://github.com/knossos-project/knossos_utils>`_
* `KNOSSOS <http://knossostool.org/>`_ is used for visualization and annotation of 3D EM data sets.
For installation instructions see :ref:`installation <installation>`. We recommend using `Anaconda <https://www.continuum.io/downloads>`_ as
python distribution.
An code example can be found in section :ref:`examples <examples>`
<spanid="id1"></span><h1>Examples<aclass="headerlink"href="#examples"title="Permalink to this headline">¶</a></h1>
<p>This page gives a brief example for a full run of then main components of SyConn.
The corresponding code is available in <codeclass="docutils literal notranslate"><spanclass="pre">/examples/full_run_example.py</span></code> and
the example data with all pre-trained models can be found in <codeclass="docutils literal notranslate"><spanclass="pre">SyConnDenseCube(.zip)</span></code> (go to our <aclass="reference external"href="http://structuralneurobiologylab.github.io/SyConn/">Website</a> for Download).</p>
<divclass="section"id="running-the-example">
<h2>Running the example<aclass="headerlink"href="#running-the-example"title="Permalink to this headline">¶</a></h2>
<p>Given that the example zipfile was extracted, running the analysis would just be:</p>
<h2>System Requirements & Installation<aclass="headerlink"href="#system-requirements-installation"title="Permalink to this headline">¶</a></h2>
<ulclass="simple">
<li>Python 2.7</li>
<li>The whole pipeline was designed and tested on Linux systems (CentOS, Arch)</li>
<li>SyConn is based on the packages <aclass="reference external"href="http://elektronn.org">ELEKTRONN</a>, <aclass="reference external"href="https://github.com/knossos-project/knossos_python_tools">Knossos Python-Tools</a></li>
<li><aclass="reference external"href="http://knossostool.org/">KNOSSOS</a> is used for visualization and annotation of 3D EM data sets.</li>
</ul>
<p>For installation instructions see <aclass="reference internal"href="installation.html#installation"><spanclass="std std-ref">installation</span></a>. We recommend using <aclass="reference external"href="https://www.continuum.io/downloads">Anaconda</a> as
python distribution.</p>
<p>An code example can be found in section <aclass="reference internal"href="Examples.html#examples"><spanclass="std std-ref">examples</span></a></p>
