Definition
The ability to reproduce an experimental result is the foundation of scientific inquiry. Similarly, computational studies need to be reproducible to serve the advance of science. However, computational scientists often find it difficult to reproduce published computational results. Here we provide an overview of efforts to support model reproducibility in computational neuroscience.
Detailed Description
Reproducing the simulation results of computational models and establishing the provenance of results should be straightforward given that computational studies do not suffer from the measurement errors seen in the experimental sciences. However, computational science has its own challenges for reproducibility, which are described well by Crook et al. (2013). In particular, issues such as the sensitivity of a model to numerics or the publication of models that are computationally under-specified lead to the need for criteria for successful model reproduction in many cases....
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Crook, S. (2020). Model Reproducibility: Overview. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_763-2
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DOI: https://doi.org/10.1007/978-1-4614-7320-6_763-2
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Publisher Name: Springer, New York, NY
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Chapter history
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Latest
Model Reproducibility: Overview- Published:
- 17 July 2020
DOI: https://doi.org/10.1007/978-1-4614-7320-6_763-2
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Original
Model Reproducibility: Overview- Published:
- 21 February 2014
DOI: https://doi.org/10.1007/978-1-4614-7320-6_763-1