Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

Frontiers in Neuroinformatics
Tiina ManninenMarja-Leena Linne

Abstract

The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures. The replicability and reproducibility issues are considered for computational models that are equally diverse, including the models for intracellular signal transduction of neurons and glial cells, in addition to single glial cells, neuron-glia interactions, and selected examples of spiking neuronal networks. We also address the comparability of the simulation results with one another to comprehend if the studied models can be used to answer similar research questions. In addition to presenting the challenges in reproducibility and replicability of published results in computational neuroscience, we highlight the need for...Continue Reading

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Citations

Jun 21, 2018·Basic & Clinical Pharmacology & Toxicology·Marja-Leena Linne
Aug 21, 2019·ELife·Marcel StimbergDan Fm Goodman
Nov 21, 2018·Frontiers in Neuroinformatics·Inga BlundellJochen Martin Eppler
Feb 25, 2021·Cerebral Cortex·Roman BauerMarcus Kaiser

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Methods Mentioned

BETA
PCA

Software Mentioned

MCell
LEMS
PyNN
Dizzy
NeuroML
GENESIS
Gepasi
PyNEST
Systems Biology Markup Language ( SBML )
Kinetikit

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