Full datasets and simulation code

Short- and long-range connections differentially modulate the small-world network’s dynamics and state

Simon Arvin^1,2,†,*, Andreas Nørgaard Glud^1,† and Keisuke Yonehara^2,*

¹ Center of Experimental Neuroscience – CENSE, Department of Neurosurgery, Institute of Clinical Medicine, Aarhus University Hospital

² Danish Research Institute of Translational Neuroscience – DANDRITE, Nordic-EMBL Partnership for Molecular Medicine, Department of Biomedicine, Aarhus University, Ole Worms Allé 8, 8000 Aarhus C, Denmark

Correspondence:

Simon Arvin, [email protected]

Keisuke Yonehara, [email protected]

doi: 10.3389/fncom.2021.783474

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Contents:

Datasheets and simulation code:

• Generative small-world graphs + topological analysis
• Kuramoto's coupled oscillators on the small-world graph

Set-up:

• Installation
• Tests
• Requisites
• Authors

Installation

Download the datasets and simulation codes by cloning the repository:

git clone https://github.com/simonarvin/connectivity_smallworld.git

You may want to use a Conda or Python virtual environment to test this code, to avoid mixing up with your system dependencies.

Using pip and a virtual environment:

python -m venv venv

source venv/bin/activate

(venv) pip install .

Remember to cd [path] to the root dataset directory.

How to create a virtual environment in Windows.

Alternatively, see the requisites list.

Tests

• Reproduce small-world data:

python small_world/smallworld_simulation.py

• Reproduce small-world figures:

python small_world/smallworld_analysis.py

• Reproduce Kuramoto data:

python kuramoto/kuramoto_simulation.py

• Reproduce Kuramoto figures:

python kuramoto/kuramoto_analysis.py

• Reproduce Kuramoto stability/attraction data:

python kuramoto/kuramoto_simulation_stability.py

• Reproduce Kuramoto stability/attraction figures:

python kuramoto/kuramoto_analysis_stability.py

• Reproduce Kuramoto predictive power graph (S1):

python kuramoto/kuramoto_analysis_PPS.py

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Requisites:

• Python 3.x (https://www.python.org/)
• networkx 2.6.x (https://networkx.org/)
• numpy 1.20.x (https://numpy.org/)
• scipy 1.5.0 (https://www.scipy.org/)
• pandas 1.3.x (https://pandas.pydata.org/)
• dominance-analysis 1.1.x (https://github.com/dominance-analysis/dominance-analysis)
• ppscore 1.2.0 (https://github.com/8080labs/ppscore/)

e.g., pip install networkx==2.6.x

Authors:

• Simon Arvin, MD Hons candidate ([email protected])
• Andreas Nørgaard Glud, MD PhD ([email protected])
• Keisuke Yonehara, DVM PhD ([email protected])

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