Commit 3f15a384 authored by Sven Dorkenwald's avatar Sven Dorkenwald Committed by GitHub
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Include publication

parent 6d99d5aa
......@@ -4,8 +4,31 @@ Synaptic connectivity inference toolkit developed at the Max-Planck-Institute fo
Max-Planck-Institute of Neurobiology, Munich <br />
Authors: Sven Dorkenwald, Philipp Schubert, Joergen Kornfeld <br />
If you use parts of this code base in your academic projects, please cite the corresponding publication appearing in Nature Methods at the end of February 2017. <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 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
}
```
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