Journal article
Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows
eLife, Vol.11, e69013
07/06/2022
DOI: 10.7554/eLife.69013
PMCID: PMC9259018
PMID: 35792600
Abstract
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.
Details
- Title: Subtitle
- Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows
- Creators
- Olivia Eriksson - KTH Royal Institute of TechnologyUpinder Singh Bhalla - National Centre for Biological SciencesKim T. Blackwell - George Mason UniversitySharon M. Crook - Arizona State UniversityDaniel Keller - École Polytechnique Fédérale de LausanneAndrei Kramer - KTH Royal Institute of TechnologyMarja-Leena Linne - Tampere UniversityAusra Saudargiene - Lithuanian University of Health SciencesRebecca C. Wade - Heidelberg Institute for Theoretical StudiesJeanette Hellgren Kotaleski - KTH Royal Institute of Technology
- Resource Type
- Journal article
- Publication Details
- eLife, Vol.11, e69013
- DOI
- 10.7554/eLife.69013
- PMID
- 35792600
- PMCID
- PMC9259018
- NLM abbreviation
- Elife
- ISSN
- 2050-084X
- eISSN
- 2050-084X
- Publisher
- eLIFE SCIENCES PUBL LTD
- Number of pages
- 31
- Grant note
- DOI: 10.13039/100010661, name: Horizon 2020 Framework Programme, award: 945539 (The Human Brain Project); DOI: 10.13039/501100004359, name: Swedish Research Council, award: VR-M-2017-02806; DOI: 10.13039/501100004359, name: Swedish Research Council, award: VR-M-2020-01652; DOI: 10.13039/100017156, name: Swedish e-Science Research Centre, award: Governmental grant for strategic research areas; name: Digital Futures, award: Governmental grant for strategic research areas; DOI: 10.13039/501100001502, name: Department of Atomic Energy, Government of India, award: No. RTI 4006; name: J.C. Bose Fellowship; DOI: 10.13039/100000027, name: National Institute on Alcohol Abuse and Alcoholism, award: R01 016022; DOI: 10.13039/100000026, name: National Institute on Drug Abuse, award: R01 038890; DOI: 10.13039/100000070, name: National Institute of Biomedical Imaging and Bioengineering, award: R01MH106674; DOI: 10.13039/100000070, name: National Institute of Biomedical Imaging and Bioengineering, award: R01DC019278; DOI: 10.13039/501100020899, name: Board of the Swiss Federal Institutes of Technology, award: Blue Brain Project; DOI: 10.13039/501100002341, name: Academy of Finland, award: Nos. 297893 and 318879; DOI: 10.13039/501100007316, name: Klaus Tschira Foundation; DOI: 10.13039/501100004504, name: Research Council of Lithuania, award: No. S-FLAG-ERA-20-1/2020-PRO-28
- Language
- English
- Date published
- 07/06/2022
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Iowa Neuroscience Institute
- Record Identifier
- 9984446459102771
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