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Current Practice in Software Development for Computational Neuroscience and How to Improve It

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  • Marc-Oliver Gewaltig
  • Robert Cannon

Abstract

Almost all research work in computational neuroscience involves software. As researchers try to understand ever more complex systems, there is a continual need for software with new capabilities. Because of the wide range of questions being investigated, new software is often developed rapidly by individuals or small groups. In these cases, it can be hard to demonstrate that the software gives the right results. Software developers are often open about the code they produce and willing to share it, but there is little appreciation among potential users of the great diversity of software development practices and end results, and how this affects the suitability of software tools for use in research projects. To help clarify these issues, we have reviewed a range of software tools and asked how the culture and practice of software development affects their validity and trustworthiness.We identified four key questions that can be used to categorize software projects and correlate them with the type of product that results. The first question addresses what is being produced. The other three concern why, how, and by whom the work is done. The answers to these questions show strong correlations with the nature of the software being produced, and its suitability for particular purposes. Based on our findings, we suggest ways in which current software development practice in computational neuroscience can be improved and propose checklists to help developers, reviewers, and scientists to assess the quality of software and whether particular pieces of software are ready for use in research.

Suggested Citation

  • Marc-Oliver Gewaltig & Robert Cannon, 2014. "Current Practice in Software Development for Computational Neuroscience and How to Improve It," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-9, January.
    • Handle: RePEc:plo:pcbi00:1003376
    DOI: 10.1371/journal.pcbi.1003376
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    References listed on IDEAS

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    1. Susan M Baxter & Steven W Day & Jacquelyn S Fetrow & Stephanie J Reisinger, 2006. "Scientific Software Development Is Not an Oxymoron," PLOS Computational Biology, Public Library of Science, vol. 2(9), pages 1-4, September.
    2. Robert C Cannon & Cian O'Donnell & Matthew F Nolan, 2010. "Stochastic Ion Channel Gating in Dendritic Neurons: Morphology Dependence and Probabilistic Synaptic Activation of Dendritic Spikes," PLOS Computational Biology, Public Library of Science, vol. 6(8), pages 1-18, August.
    3. Darrel C. Ince & Leslie Hatton & John Graham-Cumming, 2012. "The case for open computer programs," Nature, Nature, vol. 482(7386), pages 485-488, February.
    4. Erik De Schutter, 2008. "Why Are Computational Neuroscience and Systems Biology So Separate?," PLOS Computational Biology, Public Library of Science, vol. 4(5), pages 1-6, May.
    5. Bryn Nelson, 2009. "Data sharing: Empty archives," Nature, Nature, vol. 461(7261), pages 160-163, September.
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    1. Serghei Mangul & Thiago Mosqueiro & Richard J Abdill & Dat Duong & Keith Mitchell & Varuni Sarwal & Brian Hill & Jaqueline Brito & Russell Jared Littman & Benjamin Statz & Angela Ka-Mei Lam & Gargi Da, 2019. "Challenges and recommendations to improve the installability and archival stability of omics computational tools," PLOS Biology, Public Library of Science, vol. 17(6), pages 1-16, June.

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