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Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0003-0740-4318
Tata Inst Fundamental Res, Natl Ctr Biol Sci, Bangalore, Karnataka, India..
George Mason Univ, Volgenau Sch Engn, Dept Bioengn, Fairfax, VA 22030 USA..ORCID iD: 0000-0003-4711-2344
Arizona State Univ, Sch Math & Stat Sci, Tempe, AZ USA..
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2022 (English)In: eLIFE, E-ISSN 2050-084X, Vol. 11, article id e69013Article, review/survey (Refereed) Published
Abstract [en]

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data - such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles - also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

Place, publisher, year, edition, pages
eLife Sciences Publications, Ltd , 2022. Vol. 11, article id e69013
Keywords [en]
FAIR, modeling workflows, parameter estimation, mathematical modeling, uncertainty quantification, synaptic plasticity
National Category
Bioinformatics and Computational Biology Neurology
Identifiers
URN: urn:nbn:se:kth:diva-315837DOI: 10.7554/eLife.69013ISI: 000822556000001PubMedID: 35792600Scopus ID: 2-s2.0-85134361130OAI: oai:DiVA.org:kth-315837DiVA, id: diva2:1684128
Note

QC 20220721

Available from: 2022-07-21 Created: 2022-07-21 Last updated: 2025-02-05Bibliographically approved

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Eriksson, OliviaKramer, AndreiHellgren Kotaleski, Jeanette

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Eriksson, OliviaBlackwell, Kim T.Kramer, AndreiLinne, Marja-LeenaHellgren Kotaleski, Jeanette
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Bioinformatics and Computational BiologyNeurology

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