Document
NOT PEER REVIEWED
Technical report
Call for community review of NeuroML — A Model Description Language for Computational Neuroscience
Published 18 Jan 2019
(https://doi.org/10.7490/f1000research.1116398.1)
Author Affiliations
Copyright © 2019 Martone M et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Published 18 Jan 2019
Technical report
Call for community review of NeuroML — A Model Description Language for Computational Neuroscience
[version 1; not peer reviewed]Author Affiliations
Abstract
Competing Interests
No competing interests were disclosed
Keywords
computational neuroscience, modelling language, standard, best practice, INCF
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What's more, in principle .mod files should be swappable between different models in a "plug-and-play" type fashion, but this has not happened. Instead, multiples of models are available for any given channel, often without proper ways to compare. Worse, in cases when models were reused, they often feature changes, that are implemented with no or only poor documentation. Details such as species/cell specificity and temperature dependencies have often been ignored or misused compared to the original implementation.
NeuroML provides a possible remedy for this. In addition to providing an extensible, machine readable description of models and their evaluation, it offers systematic ways to include metadata and to compare models. Hopefully, these design decisions will minimize duplication and confusion and enable constructive growth in the long run. As neuroscience modelling becomes more widely accepted, it is imperative that we adopt a model description standard which is programming language-agnostic. NeuroML is one such well defined standard, with a team of editors and supporting tools. For these reasons, I endorse NeuroML as a community standard for model descriptions.
For context, I am involved in the Ion Channel Genealogy project, available at www.icg.neurotheory.ox.ac.uk. The project centers on curating ion channel meta-data, in order to quantify and compare their function, to eventually incorporate all of them into NeuroML. My feedback is based on inspection of the mechanism files (.mod) for voltage gated ion channels on ModelDB.
What's more, in principle .mod files should be swappable between different models in a "plug-and-play" type fashion, but this has not happened. Instead, multiples of models are available for any given channel, often without proper ways to compare. Worse, in cases when models were reused, they often feature changes, that are implemented with no or only poor documentation. Details such as species/cell specificity and temperature dependencies have often been ignored or misused compared to the original implementation.
NeuroML provides a possible remedy for this. In addition to providing an extensible, machine readable description of models and their evaluation, it offers systematic ways to include metadata and to compare models. Hopefully, these design decisions will minimize duplication and confusion and enable constructive growth in the long run. As neuroscience modelling becomes more widely accepted, it is imperative that we adopt a model description standard which is programming language-agnostic. NeuroML is one such well defined standard, with a team of editors and supporting tools. For these reasons, I endorse NeuroML as a community standard for model descriptions.
For context, I am involved in the Ion Channel Genealogy project, available at www.icg.neurotheory.ox.ac.uk. The project centers on curating ion channel meta-data, in order to quantify and compare their function, to eventually incorporate all of them into NeuroML. My feedback is based on inspection of the mechanism files (.mod) for voltage gated ion channels on ModelDB.
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