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https://github.com/bluebrain/reliability-and-structure

https://github.com/bluebrain/reliability-and-structure

Science Score: 26.0%

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Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: BlueBrain
  • License: agpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 73.8 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme License

README.md

Heterogeneous and non-random cortical connectivity undergirds efficient, robust and reliable neural codes

Study of network structure and how it shapes the robustness - reliability - efficiency struggle in biological neural networks as described in this publication.

DOI:10.1101/2024.03.15.585196

The repository is structured as follows:

  • library: Library of functions for all the analyses related to the publication except for classficiation
  • data_analysis: In this directory we provide all the scripts use to compute the different network metrics and their relation to function.
    • structural: Subdirectory where the analysis of purely structural properties for all connectomes and their corresponding controls is performed.
      • code: Scripts that generate the data. README
      • visualizationandnotebooks: Scripts or notebooks to visualize data or generate figures
    • activity: Subdirectory where the analysis of properties that relate to function or link function to structure in BBP and MICrONS is performed.
      • computation: Scripts that generate the data. README
      • visualization: Scripts or notebooks to visualize data or generate figures
  • classification: Pipeline for stimulus classification with two classes of featurizations based on: PCA of the activity or network properties of active subgraphs.
    • PCA_method: README
    • network_based: README
    • visualization: Visualization of the classficiation results.

Local README files provide a description of the scripts used for computation.

Citation

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

@article{egas2024efficiency, title={Heterogeneous and non-random cortical connectivity undergirds efficient, robust and reliable neural codes}, 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} }

Funding & Acknowledgment

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) 2024 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

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Dependencies

classification/PCA_method/requirements.txt pypi
  • future *
  • h5py *
  • networkx ==2.6.3
  • numpy ==1.21.6
  • pandas ==1.3.5
  • pickle5 *
  • progressbar *
  • pyflagser *
  • scikit-learn *
  • scipy >=1.0.0
  • simplejson *
classification/network_based/requirements.txt pypi
  • concurrent *
  • json *
  • networkx *
  • numpy *
  • os *
  • pandas *
  • pickle *
  • pyflagser *
  • pyflagsercount *
  • scipy *
  • subprocess *
  • sys *
  • time *