1. Neuroscience
Download icon

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
Tools and Resources
  • Cited 8
  • Views 4,050
  • Annotations
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.

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.

Metrics

  • 4,050
    Page views
  • 478
    Downloads
  • 8
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    Cristina Blázquez et al.
    Research Article

    The use of cannabis is rapidly expanding worldwide. Thus, innovative studies aimed to identify, understand and potentially reduce cannabis-evoked harms are warranted. Here, we found that Δ9-tetrahydrocannabinol, the psychoactive ingredient of cannabis, disrupts autophagy selectively in the striatum, a brain area that controls motor behavior, both in vitro and in vivo. Boosting autophagy, either pharmacologically (with temsirolimus) or by dietary intervention (with trehalose), rescued the Δ9-tetrahydrocannabinol-induced impairment of motor coordination in mice. The combination of conditional knockout mouse models and viral vector-mediated autophagy-modulating strategies in vivo showed that cannabinoid CB1 receptors located on neurons belonging to the direct (striatonigral) pathway are required for the motor-impairing activity of Δ9-tetrahydrocannabinol by inhibiting local autophagy. Taken together, these findings identify inhibition of autophagy as an unprecedented mechanistic link between cannabinoids and motor performance, and suggest that activators of autophagy might be considered as potential therapeutic tools to treat specific cannabinoid-evoked behavioral alterations.

    1. Developmental Biology
    2. Neuroscience
    Konstantinos Lagogiannis et al.
    Research Article

    Goal-directed behaviours may be poorly coordinated in young animals but, with age and experience, behaviour progressively adapts to efficiently exploit the animal's ecological niche. How experience impinges on the developing neural circuits of behaviour is an open question. We have conducted a detailed study of the effects of experience on the ontogeny of hunting behaviour in larval zebrafish. We report that larvae with prior experience of live prey consume considerably more prey than naive larvae. This is mainly due to increased capture success and a modest increase in hunt rate. We demonstrate that the initial turn to prey and the final capture manoeuvre of the hunting sequence were jointly modified by experience and that modification of these components predicted capture success. Our findings establish an ethologically relevant paradigm in zebrafish for studying how the brain is shaped by experience to drive the ontogeny of efficient behaviour.