The NeuroML ecosystem for standardized multi-scale modeling in neuroscience

Area of Science:

• Computational Neuroscience
• Systems Neuroscience
• Computational Biology

Background:

• Data-driven models of neurons and circuits are crucial for understanding brain function and disease.
• Constructing and reusing complex, biologically detailed neural models is challenging due to inherent biological complexity and fragmented modeling tools.
• Existing tools lack interoperability, hindering the integration of data-driven models into research workflows.

Purpose of the Study:

• To introduce the NeuroML ecosystem as a solution to the fragmentation of computational neuroscience modeling tools.
• To demonstrate how NeuroML facilitates the construction, testing, and analysis of standardized neural system models.
• To highlight NeuroML's support for FAIR data principles, promoting open and reproducible science.

Main Methods:

• Development and evolution of NeuroML as a mature model description language standard.
• Creation of an interoperable ecosystem of open-source software tools for model creation, visualization, validation, and simulation.
• Integration of the NeuroML ecosystem into research workflows for streamlined model development and analysis.

Main Results:

• NeuroML has become a community standard encompassing diverse modeling approaches in computational neuroscience.
• A rich ecosystem of interoperable open-source tools supports the entire lifecycle of data-driven model development.
• The NeuroML ecosystem simplifies the creation, testing, and analysis of standardized neural models.

Conclusions:

• The NeuroML ecosystem effectively addresses the challenges of constructing and reusing complex neural models.
• By supporting FAIR principles, NeuroML promotes transparency, reproducibility, and collaboration in computational neuroscience research.
• Incorporating NeuroML into research workflows enhances the efficiency and standardization of neural system modeling.