SACS: A Reproducible, Configuration-Driven Software Framework for Schematic Multi-Region Circuit Simulation and...

Area of Science:

• Computational Neuroscience
• Neuroinformatics
• Scientific Software Engineering

Background:

• Computational neuroscience research often involves complex, loosely coupled workflows.
• Lack of reproducibility hinders scientific progress and collaboration.
• Existing tools present challenges in managing simulation code, configurations, and provenance.

Purpose of the Study:

• To introduce SACS (Simulation Analysis and Configuration System), a novel framework for reproducible schematic multi-region circuit simulation.
• To enhance transparency, inspectability, and reusability in computational neuroscience experiments.
• To provide an integrated environment for configuration, execution, analytics, provenance, and replay.

Main Methods:

• SACS is a configuration-driven research-software framework implemented as the Python package brain_sim.
• It converts declarative model and scenario specifications into standardized run directories.
• Key features include integrated analytics, validation checks, deterministic execution, and replay mechanisms.

Main Results:

• SACS generates standardized run directories containing numerical outputs, summaries, figure recipes, validation reports, and provenance metadata.
• The framework facilitates encoding hypotheses as explicit configurations for iterative experimentation.
• Evaluation focuses on software performance for reproducible computational experimentation, not biological validation.

Conclusions:

• SACS significantly improves transparency, inspectability, and reuse for schematic circuit experiments.
• By integrating configuration, execution, analytics, provenance, and replay, SACS streamlines computational neuroscience workflows.
• The framework supports a more robust and iterative approach to computational experimentation.