Arbor

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Welcome to the documentation for Arbor, the multi-compartment neural network simulation library.

You can find out how to get Arbor; get started quickly with our tutorials; or continue reading to learn more about Arbor.

What is Arbor?

Arbor is a high-performance library for computational neuroscience simulations with multi-compartment, morphologically-detailed cells, from single cell models to very large networks. Arbor is written from the ground up with many-cpu and gpu architectures in mind, to help neuroscientists effectively use contemporary and future HPC systems to meet their simulation needs.

Arbor supports NVIDIA and AMD GPUs as well as explicit vectorization on CPUs from Intel (AVX, AVX2 and AVX512) and ARM (Neon and SVE). When coupled with low memory overheads, this makes Arbor an order of magnitude faster than the most widely-used comparable simulation software.

Arbor is open source and openly developed, and we use development practices such as unit testing, continuous integration, and validation.

Documentation organisation

  • Features is an index of Arbor’s major neuroscientific features and a collection of best practices and experiences collected from the Arbor modelling community, meant to spread information on how to solve common modelling questions in Arbor.

  • Tutorials contains a few ready-made examples you can use to quickly get started using Arbor. In the tutorial descriptions we link to the relevant Arbor concepts.

  • Concepts overview describes the design and concepts used in Arbor. The breakdown of concepts is mirrored (as much as possible) in the Python and C++, so you can easily switch between languages and concepts.

  • The API section details our Python and C++ API. The Developers Guide describes Arbor code that is not user-facing; convenience classes, architecture abstractions, and other information that is relevant to understanding the inner workings of Arbor and the mathematical foundations underpinning the engine.

  • Contributions to Arbor are very welcome! Under Contributing you’ll find the conventions and procedures for all kinds of contributions.

Citing Arbor

The Arbor software can be cited by version via Zenodo or via Arbors introductory paper.

Latest version

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Version 0.10.0

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Version 0.9.0

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Version 0.8.1

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Version 0.8

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Version 0.7

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Version 0.6

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Version 0.5.2

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Version 0.2

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Version 0.1

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Introductory paper

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A preprint is available at arXiv.

Cite (Bibtex format)

Introductory paper and latest version on Zenodo:

@INPROCEEDINGS{
    paper:arbor2019,
    author={N. {Abi Akar} and B. {Cumming} and V. {Karakasis} and A. {Küsters} and W. {Klijn} and A. {Peyser} and S. {Yates}},
    booktitle={2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
    title={{Arbor --- A Morphologically-Detailed Neural Network Simulation Library for Contemporary High-Performance Computing Architectures}},
    year={2019}, month={feb}, volume={}, number={},
    pages={274--282},
    doi={10.1109/EMPDP.2019.8671560},
    ISSN={2377-5750}}

@software{arbor_0_10_0,
  author       = {Cumming, Benjamin and
                  Yates, Stuart and
                  Hater, Thorsten and
                  Lu, Han and
                  Huisman, Brent and
                  Wouter, Klijn and
                  Bösch, Fabian and
                  Frasch, Simon and
                  de Schepper, Robin and
                  Luboeinski, Jannik},
  title        = {Arbor v0.10.0},
  month        = aug,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {v0.10.0},
  doi          = {10.5281/zenodo.13284789},
  url          = {https://doi.org/10.5281/zenodo.13284789}
}

@software{nora_abi_akar_2023_8233847,
  author       = {Nora Abi Akar and
                  John Biddiscombe and
                  Benjamin Cumming and
                  Marko Kabic and
                  Vasileios Karakasis and
                  Wouter Klijn and
                  Anne Küsters and
                  Alexander Peyser and
                  Stuart Yates and
                  Thorsten Hater and
                  Brent Huisman and
                  Espen Hagen and
                  Robin De Schepper and
                  Charl Linssen and
                  Harmen Stoppels and
                  Sebastian Schmitt and
                  Felix Huber and
                  Max Engelen and
                  Fabian Bösch and
                  Jannik Luboeinski and
                  Simon Frasch and
                  Lukas Drescher and
                  Lennart Landsmeer},
  title        = {Arbor Library v0.9.0},
  month        = nov,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v0.9.0},
  doi          = {10.5281/zenodo.8233847},
  url          = {https://doi.org/10.5281/zenodo.8233847}
}

Acknowledgements

This research has received funding from the European Unions Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270 (Human Brain Project SGA1), Specific Grant Agreement No. 785907 (Human Brain Project SGA2), and Specific Grant Agreement No. 945539 (Human Brain Project SGA3).

Arbor is an eBrains project.

A full list of our software attributions can be found here.

Get started:

Concepts:

Cell Types:

File formats:

API reference: