hladnik_grewe_heterogeneity

Hladnik & Grewe: Receptive field sizes and neuronal encoding bandwidth are constrained by axonal conduction delays.

This repository contains raw data of neuronal activity in P-type electroreceptors of the electric fish Apteronotus leptorhynchus, derived data i.e. analysis results, the python scripts running the analyses, simulations and the plotting.

raw_data

This folder contains the raw datasets. Data and metadata are stored in the open NIX container format. To read these data files you need the nixio python library. For a more comfortable approach use the rlxnix python package. Usually, the files contain the raw membrane potential recording, the detected action potentials, the animal's electric organ discharge (EOD) together with the respective metadata. Note: Some datasets are older and have been used in previously analyses. The dataset structure deviates a bit from newer files.

derived_data

This folder contains the analysis results. The contained files will be overwritten when re-running the analysis script. *csv-*files are written with pandas using the semicolon as delimiter to read them use df = pd.read_csv(filename, sep=";", index_col=0). *npz-*files are compressed numpy files.

stimuli

Contains a single file, which is the white noise stimulus waveform.

file_lists

Two simple text files:

1. listing the datasets in which the receptive field was measured, and from which we extract the baseline response properties.
2. The datasets in which white-noise responses were measured.

figures

Contains some png files which will be embedded into the final figures. The folder is the default output folder for created figures.

code package

Three sub-packages:

1. analyses: The data analysis scripts working on the raw data
2. plots: Plotting scripts
3. simulations: LIF model simulations

Scripts

To run the analyses, simulations, or plot the figures, you can use one of the three scripts"

1. run_analyses.py command line tool for running the all data analyses. (Note! analysis of the heterogeneous populations will take a while)
2. run_simulations.py command line tool to run the LIF simulations. (Note! This will take a while)
3. plot_figures.py command line script that allows to plot the figures of the publication.

The first two have only a single argument which is the number of parallel processes that should be spawned for the respective task. E.g.

python3 run_simulations.py --help  # to show the help text
python3 run_simulations.py -j 12  # run the simulations using 12 parallel processes

The plot tool takes that defines which sub command (i.e. figure) will be run. Each of these take a few more arguments that allow to select which figure should be plotted.

python3 plot_figures.py --help  # to show list of all subcommands

For example:

python3 plot_figures.py figure1 -h  # to show the options for figure 1
python3 plot_figures.py figure1 -n  # to plot figure 1 and show it.

If the -n argument is given, the figure will be shown but not saved. If the option is omitted, the figure will be saved to the figures folder, but not shown. The default arguments should lead to the same figures as in the paper.

Dependencies

The scripts depend on the following packages (installed version):

• python (3.8.10)
• numpy (1.19.2)
• pandas (1.1.0)
• scipy (1.7.0)
• nixio (1.5.2)
• rlxnix (0.6.10)
• joblib (0.16.0)
• sklearn (0.23.1)
• matplotlib (3.4.2)