corr_2024
This repository contains all the codes used for the paper: "Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanisms"
Science Score: 44.0%
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Low similarity (12.1%) to scientific vocabulary
Repository
This repository contains all the codes used for the paper: "Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanisms"
Basic Info
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Metadata Files
README.md
Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanisms
What do you find in this repository?
In this repository, you will be able to find all codes and data that were involved in Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanisms.
How to use the codes?
In this work, the Julia programming language was used. To use the codes, first download the latest version of Julia here if it is not already the case.
Once this is done, you can run dependencies.jl to download and install all the packages used in this work. To do so, open Julia, cd to the folder where you can find dependencies.jl and run
jl
include("dependencies.jl")
How are the codes organized?
In this repository, you have one folder concerning the codes for the stomatogastric neuron model (STG) and another one for the dopaminergic neuron (DA) model. The code structure is the same for both models.
In both folders, you will find .jl files in which the model equations or the DICs method is implemented, as well as other useful functions. You will also find a data folder in which all the data necessary to reproduce the figures of the article are already generated. Finally, two notebooks ipynb are also available. One is called data generator, this one contains all the codes necessary to generate all the .dat files in the data folder, this may take a while depending on your computer. The other one is called plots and basically include the .dat files to produce the figures of the article.
Special note to launch Jupyter with Julia
To open JupyterNotebook/JupyterLab using Julia, execute in Julia
jl
using IJulia
notebook() # Or jupyterlab()
Then simply browse to the .ipynb files to run them. If it is the first time you ever launch Jupyter, Julia will ask you if you want to install it through Conda. Accept to start the installation of Jupyter.
Codes written by Arthur Fyon
Owner
- Name: Arthur Fyon
- Login: arthur-fyon
- Kind: user
- Company: University of Liège
- Repositories: 1
- Profile: https://github.com/arthur-fyon
PhD candidate in biomedical engineering
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Fyon
given-names: Arthur
orcid: https://orcid.org/0009-0008-0771-8767
title: "CORR_2024"
version: 1.0.0
identifiers:
- type: doi
value: 10.5281/zenodo.10842029
date-released: 2024-03-20
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