CRN Model Simulation Suite (R12-CROWN)

Simulation Suite for Coherent Resonant Netting (CRN) Overview This repository contains the complete simulation source code (Python), figure-generation scripts, and precomputed numerical artifacts supporting the hypothesis and theory article: Thermodynamic Advantage of Transient Wave Dynamics in Hierarchical Decision Architectures (Preprint / Submitted to Frontiers in Computational Neuroscience) The software suite, internally designated as R12-CROWN, implements the open-system GKSL/Lindblad dynamics described in the manuscript to demonstrate the "Environment-Assisted Quantum Transport" (ENAQT-like) regime in neural decision circuits. Repository Contents The dataset allows for full reproducibility of all figures (Fig. 2–6) presented in the paper. R12-CROWN/ The core simulation engine (CRN_SIM). Includes the GKSL solver, graph topology generators (Chain, Small-World, Modular), and the main plotting scripts for spectral gaps and time-domain evolution. R12-CROWN-LargeN-N1000/ Code and heavy data outputs for the large-scale stress test ($N=1000$ Watts-Strogatz networks), validating the robustness of the resonance window. artifacts/ A consolidated mirror of the final generated figures (.png/.pdf) and aggregate .csv data files used for analysis. Naming Convention Note Important: The directories in this archive are named R12-CROWN mirroring the internal development version. In the associated manuscript, this software is referred to as CRN_SIM. Please maintain the existing directory structure to ensure path compatibility for the reproducibility scripts. System Requirements Python 3.8+ Standard scientific stack: numpy, scipy, matplotlib, networkx, qutip (optional, for benchmark verification).