Large-scale mapping of axonal arbors using high-density microelectrode arrays

This repositiory contains the source code as well as the links to the example data to replicate all figures. The Hana (high density microelectrode array recording analysis) pipeline is open source, see licence. The example data (approx. 4 GB) consists of spike triggered averages and events that were extracted from the raw recordings.

If you use this library in your research, please cite our paper (Frontiers):

Bullmann T, Radivojevic M, Huber S, Deligkaris K, Hierlemann A, Frey U (2019) Large-scale mapping of axonal arbors using high-density microelectrode arrays. Accepted

@article {Bullmann2019b,
	author = {Torsten Bullmann, Milos Radivojevic, Stefan Huber, Kosmas Deligkaris, Andreas Reinhold Hierlemann, Urs Frey},
	title = {Large-scale mapping of axonal arbors using high-density microelectrode arrays},
	year = {2019},
	doi = {},
	URL = {},
	eprint = {},
	journal = {Frontiers in Cellular Neuroscience, section Cellular Neurophysiology}
}

How to use

Source code

Clone hdmea_axon from GitHub:

git clone http://github.com/tbullmann/hdmea_axon

Clone hana directly into the folder hdmea_axon/hana:

git clone http://github.com/tbullmann/hana hdmea_axon/hana

Install requirements

Using conda to create an environment hdmea (or any other name) and install the requirements:

conda create --name hana python=2.7
source activate hana
conda install --file hdmea_axon/hana/requirements.txt

Folders and data

Create these folder/symlinks as you wish:

cd publication
mkdir temp 
mkdir figures
mkdir data

Download the data from google drive and copy into the data folder. For the proper folder structure see section 'Folders' below.

Replicate the figures

For details see description of figures.

Assuming you are in hdmea_axon/publication, you append your PYTHONPATH, activate the environment hdmea and run the script from command line:

export PYTHONPATH=../:$PYTHONPATH     # otherwise cannot see /hana
source activate hana                  # use Python environment for hana
python all_figures.py 

The script takes about 10 minutes for the first run on the full data set. If the temporary files already exist, the figures itself will take only about 3 minutes. A total of 5 main and 1 supplementary figures can be found as *.png files in /figures.

Replicate the movies

You need to install a renderer for gif (ImageMagick) or mpeg (ffmpeg).

Continue by typing:

source activate hana                  # use Python environment for hana
python all_animations.py

This script takes about 15 minutes. A total of 46 movies can be found as *.gif and/or *.mp4 files in temp/culture1.

Using PyCharm

In case you are using PyCharm you have to specify the use of the project interpreter from the hdmea environment.

Folders

Folders structure and important files:

.
├── hana          (Symlink or cloned git repo)
│   └── ...
├── misc           
│   └── ...       (Old figures)
├── publication
│   ├── data      (Symlink or directory)
│   │   ├── culture1  
│   │   └── ...
│   ├── temp      (Symlink or directory)
│   │   ├── culture1  
│   │   └── ...
│   ├── figures   (Symlink or directory)
│   ├── all_figures.py
│   ├── all_animations.py
│   └── ...
├── LICENCE.md
├── README.md
└── ...