neuroc 0.3.0

NeuroC: a collection of tools for morphology cloning applications

NeuroC

A collections of programs morphology cloning The following packages can be found

• axon shrinker:

For each morphology of the FILES_FOLDER, remove the axon splice described by the corresponding annotation (ie. located between the end of the dendritic annotation and the start of the axonal annotation) and replace it by an intermediate vertical segment. For each input morphology, NSAMPLES output morphologies are generated, each with a different length of the replaced segment. Lengths spans from 0 to the length of initially spliced segment

• jitter: Create clones of a given morphology with some jitter to make them all different. There are two kinds of jitter: rotation and scaling. Rotational jitter rotates each section around its parent axis or around the PCA (Principal Component Analysis) of all descendant points.

Installation

In a fresh virtualenv:

pip install  --index-url  https://bbpteam.epfl.ch/repository/devpi/bbprelman/dev/+simple/ neuroc[plotly]

Usage

In a shell, do:

neuroc --help

to list all functionalities.

Axon shrinker

neuroc axon_shrinker files_dir annotations_dir output_dir

to shrink axons.

Rat to human scaling

neuroc scale rat-to-human HUMAN_DIR RAT_DIR MTYPE_MAPPING OUTPUT_DIR

Will scale the rat cells in RAT_DIR to human cells dimensions. HUMAN_DIR should be a dir with the following structure:

• Must be only composed of sub-folders whose filename is a layer name
• Each sub folder should be composed of morphology files whose first part of the filename before the '_' is considered as the mtype

RAT_DIR should be a directory containing rat morphology files **and a neuronDB.xml file.

MTYPE_MAPPING_FILE is a YAML file containing a dictionary where:

• a key is a human mtype or all
• the value is a list of rat mtypes to associate with the key. Or a list of one 'all' element

ls
$ RAT_DIR

$ RAT_DIR\L1
$ RAT_DIR\L2
$ ...
$ RAT_DIR\L6

$ RAT_DIR\L1\AC_cell_name.swc
$ RAT_DIR\L1\BTC_cell_name.swc
$ ...

Acknowledgements

The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.

This project/research received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Framework Partnership Agreement No. 650003 (HBP FPA).

For license and authors, see LICENSE.txt.

Copyright (c) 2013-2024 Blue Brain Project/EPFL