Computational neuroscience is a powerful ally in our quest to understand the brain. Even the most simple model can shed light on the role of this or that structure and propose new hypothesis concerning the overall brain organization. However, any model in Science is doomed to be proved wrong or incomplete and replaced by a more accurate one. In the meantime, for such replacement to happen, we have first to make sure that models are actually reproducible such that they can be tested, evaluated, criticized and ultimately modified, replaced or even rejected. This is where the shoe pinches. If we cannot reproduce a model in the first place, we’re doomed to re-invent the wheel again and again, preventing us from building an incremental computational knowledge of the brain.

References

  1. HAL | DOI | PDF | BibTeX | spotlight
    M. Topalidou, A. Leblois, T. Boraud, and N. P. Rougier, “A Long Journey into Reproducible Computational Neuroscience,” Frontiers in Computational Neuroscience, vol. 9, no. 28, 2015.