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Philipp Schubert
SyConn
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7f7b622b
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7f7b622b
authored
Oct 11, 2018
by
Philipp Schubert
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updated README and docs
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README.md
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# SyConn v2
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 branch 'dorkenwald2017nm'.
Refactored (still an early stage construction) version of SyConn for automated synaptic connectivity inference based on dense EM segmentation data.
For v1 see the SyConn branch
[
dorkenwald2017nm
](
https://github.com/StructuralNeurobiologyLab/SyConn/tree/dorkenwald2017nm
)
.
Version 2 currently features:
-
introduction of supervoxel and agglomerated supervoxel classes
...
...
@@ -12,13 +9,11 @@ Version 2 currently features:
-
[
glia identification and splitting
](
https://www.biorxiv.org/content/early/2018/07/06/364034
)
-
generation of connectivity matrix
## 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.
*
SyConn is based on the packages
[
elektronn
](
http://elektronn.org
)
_,
[
knossos-utils
](
https://github.com/knossos-project/knossos_
utils
)
is used for visualization and annotation of 3D EM data sets.
*
[
VIGRA
](
https://ukoethe.github.io/vigra/
)
, e.g.
``conda install -c ukoethe vigra``
*
osmesa, e.g.:
``conda install -c menpo osmesa``
...
...
@@ -29,6 +24,13 @@ You can install SyConn using ``git`` and ``pip``:
pip install -r requirements.txt
pip install .
## Documentation
## Tutorials & Documentation
For tutorials see
[
here
](
docs/doc.md
)
.
To build the documentation run
`make html`
in the
`docs`
folder.
# The Team
The Synaptic connectivity inference toolkit developed is developed at Max-Planck-Institute of Neurobiology, Munich.
For documentation see
[
here
](
docs/doc.md
)
\ No newline at end of file
Authors: Philipp Schubert, Sven Dorkenwald, Rangoli Saxena, Joergen Kornfeld
\ No newline at end of file
docs/analysis_parts.md
0 → 100644
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# Analysis steps
_in progress_
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docs/doc.md
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Next, we will introduce the important bits of SyConn. For an example full run see
[
here
](
example_full_run.md
)
.
# Tutorials
*
[
Working directory and config setup
](
config.md
)
...
...
@@ -8,7 +8,7 @@ Next, we will introduce the important bits of SyConn. For an example full run se
*
[
Mapping cellular organelles
](
object_mapping.md
)
to SSVs
*
[
SSO
](
super_segmentation_objects.md
)
data class
*
Data class to store agglomerated super voxels
[
SSO
](
super_segmentation_objects.md
)
*
[
Skeletons
](
skeletons.md
)
of (super) super voxel
...
...
@@ -23,4 +23,7 @@ Next, we will introduce the important bits of SyConn. For an example full run se
*
[
Synapse type
](
synapse_type.md
)
prediction
For more detailed descriptions of parts of the analysis pipeline see
[
here
](
analysis_parts.md
)
.
docs/example_full_run.md
deleted
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# Full-run example
_in progress_
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