SACS: A Reproducible, Config-Driven Framework for Schematic Multi-Region Circuit Simulation with Integrated Analytics

Computational neuroscience workflows often become difficult to reproduce when simulation code, configuration logic, analysis scripts, and visualization utilities evolve as loosely connected components. SACS is presented as a config-driven software framework for schematic multi-region circuit simulation with integrated analytics, deterministic replay, and graphical inspection. Implemented as the Python package brain_sim, the framework is designed to move from declarative scenario definition to standardized run artifacts without requiring ad hoc glue code at each stage of the workflow. The system combines structured configuration, explicit provenance capture, repeatable execution, and post-run analysis in a single environment. Rather than positioning the software as a biologically validated model, this paper presents SACS as research software for reproducible computational experiments, workflow prototyping, and teaching-oriented use cases. The architecture of the framework, the organization of its execution pipeline, and the mechanisms used to support deterministic replay and inspection are described. A benchmark scenario demonstrates workflow behavior, artifact generation, and analysis integration. The contribution is methodological, emphasizing design, reproducibility, and usability rather than clinical or biological claims.