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:
- 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
- generation of connectivity matrix
Documentation
--------------
_in progress_
# SyConn
Synaptic connectivity inference toolkit developed at the Max-Planck-Institute for Medical Research, Heidelberg and
...
...
@@ -11,7 +26,7 @@ To get started, please have a look at our [documentation](https://structuralneur
Publication
-----------
SyConn 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/>
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/>
