1. Neuroscience
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Brian 2, an intuitive and efficient neural simulator

  1. Marcel Stimberg  Is a corresponding author
  2. Romain Brette
  3. Dan FM Goodman
  1. Sorbonne Université, INSERM, CNRS, France
  2. Imperial College London, United Kingdom
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Cite this article as: eLife 2019;8:e47314 doi: 10.7554/eLife.47314

Abstract

Brian 2 allows scientists to simply and efficiently simulate spiking neural network models. These models can feature novel dynamical equations, their interactions with the environment, and experimental protocols. To preserve high performance when defining new models, most simulators offer two options: low-level programming or description languages. The first option requires expertise, is prone to errors, and is problematic for reproducibility. The second option cannot describe all aspects of a computational experiment, such as the potentially complex logic of a stimulation protocol. Brian addresses these issues using runtime code generation. Scientists write code with simple and concise high-level descriptions, and Brian transforms them into efficient low-level code that can run interleaved with their code. We illustrate this with several challenging examples: a plastic model of the pyloric network, a closed-loop sensorimotor model, a programmatic exploration of a neuron model, and an auditory model with real-time input.

Data availability

Source code to replicate Figures 1-7, as well as the simulations shown in Appendix 4, are provided in a github repository (https://github.com/brian-team/brian2_paper_examples). Source code to run benchmarks as presented in Figure 8 is provided as a supplementary file together with this submission (benchmark_code.zip)

Article and author information

Author details

  1. Marcel Stimberg

    Institut de la Vision, Sorbonne Université, INSERM, CNRS, Paris, France
    For correspondence
    marcel.stimberg@inserm.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2648-4790
  2. Romain Brette

    Institut de la Vision, Sorbonne Université, INSERM, CNRS, Paris, France
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0110-1623
  3. Dan FM Goodman

    Department of Electrical and Electronic Engineering, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1007-6474

Funding

Agence Nationale de la Recherche (Axode ANR-14-CE13-0003)

  • Romain Brette

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Frances K Skinner, Krembil Research Institute, University Health Network, Canada

Publication history

  1. Received: April 1, 2019
  2. Accepted: August 19, 2019
  3. Accepted Manuscript published: August 20, 2019 (version 1)
  4. Version of Record published: October 10, 2019 (version 2)

Copyright

© 2019, Stimberg et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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