Multiscale co-simulation design pattern for neuroscience applications.

Area of Science:

• Computational neuroscience
• Scientific simulation
• Software engineering

Background:

• Integrating data across diverse scientific sources enhances research value.
• Achieving interoperability for tools and models across spatial and temporal scales presents significant challenges.
• Current simulation frameworks often struggle to bridge different levels of biological organization.

Purpose of the Study:

• To introduce Parallel Co-Simulation, a novel toolbox for interoperation of simulators at varying scales.
• To present a software co-design pattern facilitating cross-scale simulation.
• To demonstrate the application of the toolbox in a complex neuroscience use case.

Main Methods:

• Development of the Parallel Co-Simulation toolbox.
• Implementation of a software co-design pattern for scalable simulations.
• Integration of The Virtual Brain and NEST simulators for a neuroscience example.

Main Results:

• Successfully enabled interoperation of simulators operating at different scales (cellular and population levels).
• Illustrated a workflow embedding a detailed cellular-level hippocampus region into a whole-brain network simulation.
• Demonstrated the ability to integrate multiscale knowledge within a unified simulation framework.

Conclusions:

• Parallel Co-Simulation significantly enhances the explanatory power of computational models by integrating knowledge across scales.
• The tool facilitates validation against multiscale experimental data.
• This approach opens new possibilities for studying complex biological systems through advanced simulation techniques.