https://github.com/bluebrain/emodel-generalisation

Generalisation of electrical models of neurons with MCMC

https://github.com/bluebrain/emodel-generalisation

Science Score: 62.0%

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Keywords

electrophysiology generalisation mcmc neuron
Last synced: 5 months ago · JSON representation ·

Repository

Generalisation of electrical models of neurons with MCMC

Basic Info
  • Host: GitHub
  • Owner: BlueBrain
  • License: other
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 17.2 MB
Statistics
  • Stars: 5
  • Watchers: 5
  • Forks: 5
  • Open Issues: 4
  • Releases: 16
Archived
Topics
electrophysiology generalisation mcmc neuron
Created over 2 years ago · Last pushed 12 months ago
Metadata Files
Readme Changelog Contributing License Citation Authors

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

DOI

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 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

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

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Last Year
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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
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  • Average comments per issue: 0.8
  • Average comments per pull request: 0.32
  • Merged pull requests: 59
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Past Year
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  • Pull requests: 14
  • Average time to close issues: N/A
  • Average time to close pull requests: 1 day
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  • 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
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  • AurelienJaquier (2)
  • adrien-berchet (1)
  • wvangeit (1)
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