Editorial: Reproducibility and Rigour in Computational Neuroscience

Platforms such as open source code sharing sites and model databases (Birgiolas et al., 2015; McDougal et al., 2017; Gleeson et al., 2019) provide the means for increasing the impact of models and other computational approaches through re-use and allow for further development and improvement. Here we see several approaches toward more rigorous model validation against experimental data across scales, as well as multiple resources for better parameter optimization and sensitivity analysis. Uncertainty quantification and sensitivity analysis can provide rigorous procedures to quantify how model outputs depend on parameter uncertainty. Here, authors summarize existing code generation pipelines associated with the most widely-used simulation platforms, simulator-independent multiscale model description languages, neuromorphic simulation platforms, and collaborative model development communities. 3.