Refactored (still an early stage construction) version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data. For the first version
Refactored version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data. For the first version
see 'SyConn' below. Current 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 supervoxel graph splitting
- generation of connectivity matrix
Documentation
--------------
_in progress_
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[documentation](https://structuralneurobiologylab.github.io/SyConn/documentation/), but be aware that it is currently outdated and applies only to SyConn v1.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/).
# SyConn
This version of SyConn is deprecated and should not be used anymore.
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
SyConn v1 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/>
