connectome-analysis 1.0.0

Methods for network analysis of graphs coming from brain models and activity on them

Connectome Analysis

General functions to analyze connectomes from a topological perspective.

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Documentation

See our documentation for detailed explanations including examples and tutorials.

See our source code for implementation details.

User installation

pip install git+https://github.com/openbraininstitute/connectome-analysis.git

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Citation

If you use this software, kindly use the following BibTeX entry for citation:

@article{egas2024efficiency,
  title={Efficiency and reliability in biological neural network architectures},
  author={Egas Santander, Daniela and Pokorny, Christoph and Ecker, Andr{\'a}s and Lazovskis, J{\=a}nis and Santoro, Matteo and Smith, Jason P and Hess, Kathryn and Levi, Ran and Reimann, Michael W},
  journal={bioRxiv},
  pages={2024--03},
  year={2024},
  publisher={Cold Spring Harbor Laboratory},
  doi = {10.1101/2024.03.15.585196}
}

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Acknowledgements & Funding

The development of this software 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.

Copyright (c) 2023-2024 Blue Brain Project / EPFL.
Copyright (c) 2025 Open Brain Institute.

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Development installation

• Clone this repository

• Requirements:

  • Poetry
  • gcc 9+
  • CMake
  • Python 3.9+

• Create a virtual environment and install the dependencies

poetry install

CMake may have difficulties to find the right compilers to compile the C++ code. If that is the case, you have to specify the path to the compilers yourself:

CC=/path/to/gcc CXX=/path/to/g++ poetry install

This is especially important on MacOS.

• Activate the virtual environment

poetry shell

• Testing

poetry run pytest tests

Documentation

The documentation is automatically generated from the content of the docs directory and from the docstrings of the public signatures of the source code. The documentation is updated and published as a Github project page automatically as part each release.