Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Area of Science:

• Neuroscience
• Computational Biology
• Systems Biology

Background:

• Neuroscience modeling integrates hypothesis-driven and data-driven approaches across biological scales.
• Current models face barriers in interoperability, transparency, and reusability, hindering multiscale brain understanding.
• Existing modeling philosophies and biological scales present challenges for integrating diverse computational approaches.

Purpose of the Study:

• To advocate for applying the FAIR (Findable, Accessible, Interoperable, Reusable) principles to neuroscience models and workflows.
• To address the lack of interoperability, transparency, and reusability in computational neuroscience models.
• To promote the integration of models across different biological scales and modeling philosophies.

Main Methods:

• The study proposes extending the FAIR principles, originally for data, to computational models and their associated workflows.
• It emphasizes the need for models and workflows to be Findable, Accessible, Interoperable, and Reusable.
• A classical synaptic plasticity model, the Bienenstock-Cooper-Munro rule, is used as a case study.

Main Results:

• Implementing FAIR principles for models and workflows facilitates their discovery, reuse, validation, and extension.
• This approach supports the integration of models regardless of their implementation (phenomenological vs. mechanistic) or scale.
• The Bienenstock-Cooper-Munro rule serves as an example of a model with diverse implementations suitable for FAIR application.

Conclusions:

• Adoption of FAIR principles for neuroscience models is crucial for advancing computational neuroscience.
• Interoperability and reusability of models will accelerate the understanding of complex brain systems.
• Standardizing models and workflows through FAIR principles enables robust validation and collaborative research.