https://github.com/bluebrain/emodel-generalisation
Generalisation of electrical models of neurons with MCMC
Science Score: 62.0%
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Low similarity (12.9%) to scientific vocabulary
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Repository
Generalisation of electrical models of neurons with MCMC
Basic Info
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- Stars: 5
- Watchers: 5
- Forks: 5
- Open Issues: 4
- Releases: 16
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Metadata Files
README.md
[!WARNING] The Blue Brain Project concluded in December 2024, so development has ceased under the BlueBrain GitHub organization. Future development will take place at: https://github.com/openbraininstitute/emodel-generalisation
emodel-generalisation
Generalisation of neuronal electrical models on a morphological population with Markov Chain Monte-Carlo.
This code accompanies the paper:
Installation
This code can be installed via pip from pypi with
pip install emodel-generalisation
or from github with
git clone git@github.com: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/mcmcsimplemulticomp
* 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
Owner
- Name: The Blue Brain Project
- Login: BlueBrain
- Kind: organization
- Email: bbp.opensource@epfl.ch
- Location: Geneva, Switzerland
- Website: https://portal.bluebrain.epfl.ch/
- Repositories: 226
- Profile: https://github.com/BlueBrain
Open Source Software produced and used by the Blue Brain Project
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
Computational synthesis of cortical dendritic
morphologies.
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- family-names: Arnaudon
given-names: Alexis
orcid: 'https://orcid.org/0000-0001-9486-1458'
- family-names: Reva
given-names: Maria
- family-names: Zbili
given-names: Michael
- family-names: Markram
given-names: Henry
orcid: 'https://orcid.org/0000-0001-6164-2590'
- family-names: Van Geit
given-names: Werner
orcid: 'https://orcid.org/0000-0002-2915-720X'
- family-names: Kanari
given-names: Lida
orcid: 'https://orcid.org/0000-0002-9539-5070'
identifiers:
- type: doi
value: 10.1101/2023.04.06.535923
description: The DOI of the related article.
repository-code: 'https://github.com/BlueBrain/emodel-generalisation'
abstract: >-
Variability is a universal feature among biological units such as neuronal cells as they enable a robust encoding of a high volume of information in neuronal circuits and prevent hyper synchronizations such as epileptic seizures. While most computational studies on electrophysiological variability in neuronal circuits were done with simplified neuron models, we instead focus on the variability of detailed biophysical models of neurons. With measures of experimental variability, we leverage a Markov chain Monte Carlo method to generate populations of electrical models able to reproduce the variability from sets of experimental recordings. By matching input resistances of soma and axon initial segments with the one of dendrites, we produce a compatible set of morphologies and electrical models that faithfully represent a given morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells with continuous adapting firing type and show that morphological variability is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.
keywords:
- Biophysical neuronal models
- Morpho-electrical variability
- Markov Chain Monte-Carlo
license: CC-BY-4.0
GitHub Events
Total
- Release event: 1
- Watch event: 2
- Delete event: 9
- Issue comment event: 1
- Push event: 13
- Pull request review event: 1
- Pull request event: 16
- Fork event: 2
- Create event: 11
Last Year
- Release event: 1
- Watch event: 2
- Delete event: 9
- Issue comment event: 1
- Push event: 13
- Pull request review event: 1
- Pull request event: 16
- Fork event: 2
- Create event: 11
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 5
- Total pull requests: 63
- Average time to close issues: 27 days
- Average time to close pull requests: 8 days
- Total issue authors: 2
- Total pull request authors: 6
- Average comments per issue: 0.8
- Average comments per pull request: 0.32
- Merged pull requests: 59
- Bot issues: 0
- Bot pull requests: 9
Past Year
- Issues: 0
- Pull requests: 14
- Average time to close issues: N/A
- Average time to close pull requests: 1 day
- Issue authors: 0
- Pull request authors: 3
- Average comments per issue: 0
- Average comments per pull request: 0.07
- Merged pull requests: 11
- Bot issues: 0
- Bot pull requests: 2
Top Authors
Issue Authors
- arnaudon (4)
- eleftherioszisis (1)
Pull Request Authors
- arnaudon (69)
- dependabot[bot] (12)
- eleftherioszisis (2)
- AurelienJaquier (2)
- adrien-berchet (1)
- wvangeit (1)