emodel-generalisation 0.2.14

Generalisation of neuronal electrical models with MCMC

emodel-generalisation

Generalisation of neuronal electrical models on a morphological population with Markov Chain Monte-Carlo.

This code accompanies the paper:

Arnaudon, A., Reva, M., Zbili, M., Markram, H., Van Geit, W., & Kanari, L. (2023). Controlling morpho-electrophysiological variability of neurons with detailed biophysical models. iScience, 2023.

Installation

This code can be installed via pip from pypi with

pip install emodel-generalisation

or from github with

git clone [e-mail]:BlueBrain/emodel-generalisation.git
pip install .

Documentation

The documentation can be found here: https://emodel-generalisation.readthedocs.io/en/latest/

Code structure

This code contains several modules, the most important are:

• model contains an adapted version of BlueBrain/BluePyEmodel core functionalities for evaluating electrical models, built on top of BlueBrain/BluePyOpt
• tasks contains the luigi workflows to run MCMC, adapt and generalise electrical model
• bluecellulab_evaluator contains functions to compute currents with BlueBrain/BlueCelluLab and hoc files of models
• mcmc contains the code to run MCMC sampling of electrical models
• information contains some WIP code to compute information theory measures on sampled electrical models

Examples

We provide several examples of the main functionalities of the emodel-generalisation code:

• run MCMC on a simple single compartment model in examples/mcmc/mcmc_singlecomp
• run MCMC on a simple multi-compartment model in examples/mcmc/mcmc_simple_multicomp
• run the entire generalisation worklow on a simplified version of the L5PC model of the paper in examples/workflow
• provide all the scripts necessary to reproduce the figures of the paper. For the scripts to run, one has to download the associated dataset on dataverse with the script get_data.sh in examples/paper_figures

Citation

When you use the emodel-generalisation code or method for your research, we ask you to cite:

Arnaudon, A., Reva, M., Zbili, M., Markram, H., Van Geit, W., & Kanari, L. (2023). Controlling morpho-electrophysiological variability of neurons with detailed biophysical models. iScience, 2023.

To get this citation in another format, please use the Cite this repository button in the sidebar of the code's github page.

Funding & Acknowledgment

The development of this code 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.

For license and authors, see LICENSE.txt and AUTHORS.md respectively.

Copyright 2022-2023 Blue Brain Project/EPFL