Statistical Methods for Computer Experiments with Applications in Neuroscience

Gilad Shapira; Ella Shaposhnik; David M. Steinberg

doi:10.20944/preprints201908.0246.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 21 August 2019 / Approved: 23 August 2019 / Online: 23 August 2019 (11:27:23 CEST)

Many scientific and technological problems are studied with the help of computer codes that simulate the phenomena of interest rather than via traditional laboratory experiments. Such models play an important role in neuroscience where they are used to mimic brain function from the sub-cellular to the macroscopic level. Exploration with computer models carries with it a number of statistical challenges: where to sample the input space for the simulator, how to make sense of the data that is generated, how to estimate unknown parameters in the model, how to validate a model. The simulator setting also has some unique problems and possibilities. This review paper describes statistical research on these issues and how that work might be applied to neural simulations.

statistics; in silico experiments; neural simulation; gaussian process regression; Bayesian inference

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Statistical Methods for Computer Experiments with Applications in Neuroscience

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