index.rst

Arbor

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

You can find out how to :ref:`get Arbor<in_install>`; get started quickly with our :ref:`tutorials<tutorial>`; 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

• :ref:`tutorial` 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.
• :ref:`modelintro` describes the design and concepts used in Arbor. The breakdown of concepts is mirrored (as much as possible) in the :ref:`pyoverview` and :ref:`cppoverview`, so you can easily switch between languages and concepts.
• The API section details our :ref:`pyoverview` and :ref:`cppoverview` API. :ref:`internals-overview` describes Arbor code that is not user-facing; convenience classes, architecture abstractions, etc.
• Contributions to Arbor are very welcome! Under :ref:`contribindex` describe 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

Version 0.5.2

@software{nora_abi_akar_2021_4428108,
author       = {Nora {Abi Akar} and
                John Biddiscombe and
                Benjamin Cumming and
                Felix Huber 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
                Sebastian Schmitt},
title        = {arbor-sim/arbor: Arbor Library v0.5},
month        = jan,
year         = 2021,
publisher    = {Zenodo},
version      = {v0.5},
doi          = {10.5281/zenodo.4428108},
url          = {https://doi.org/10.5281/zenodo.4428108}
}

Version 0.2

Version 0.1

Introductory paper

@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}}

Alternative citation formats for the paper can be downloaded here, and a preprint is available at arXiv.

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.