Learning from the Past: Approaches for Reproducibility in Computational Neuroscience

  • Sharon M. CrookEmail author
  • Andrew P. Davison
  • Hans E. Plesser
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI, volume 9)


Reproducible experiments are the cornerstone of science: only observations that can be independently confirmed enter the body of scientific knowledge. Computational science should excel in reproducibility, as simulations on digital computers avoid many of the small variations that are beyond the control of the experimental biologist or physicist. However, in reality, computational science has its own challenges for reproducibility: many computational scientists find it difficult to reproduce results published in the literature, and many authors have met problems replicating even the figures in their own papers. We present a distinction between different levels of replicability and reproducibility of findings in computational neuroscience. We also demonstrate that simulations of neural models can be highly sensitive to numerical details, and conclude that often it is futile to expect exact replicability of simulation results across simulator software packages. Thus, the computational neuroscience community needs to discuss how to define successful reproduction of simulation studies. Any investigation of failures to reproduce published results will benefit significantly from the ability to track the provenance of the original results. We present tools and best practices developed over the past 2 decades that facilitate provenance tracking and model sharing.


Markup Language Original Code Independent Reproduction Computational Neuroscience Version Control System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



S.C. acknowledges support from the National Institute of Mental Health under grant R01MH061905. A.P.D. acknowledges support from the Centre National de la Recherche Scientifique and from the European Union under grant FP7-ICT-FET-269921 (BrainScaleS). H.E.P. acknowledges support from the Research Council of Norway under grant 178892/V30 eNeuro.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Sharon M. Crook
    • 1
    Email author
  • Andrew P. Davison
    • 2
  • Hans E. Plesser
    • 3
  1. 1.School of Mathematical and Statistical Sciences and School of Life Sciences, Center for Adaptive Neural SystemsArizona State UniversityTempeUSA
  2. 2.Unité de Neuroscience Information et Complexité (UNIC), CNRSGif-sur-YvetteFrance
  3. 3.Department of Mathematical Sciences and TechnologyNorwegian University of Life SciencesAasNorway

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