TLMM v4.2: Identifiability, Energy-Optimal Adaptive Control, Robustness, Recovery, and Translational Roadmap for Adaptive Neuromodulation

TLMM v4.2 is an illustrative and simulation-derived adaptive neuromodulation framework integrating structural identifiability, Bayesian state estimation, energy-optimal adaptive control, robustness analysis, colored-noise-aware operable-window narrowing, recovery dynamics, and translational roadmap planning. This repository contains: - a finalized infographic-style figure report,- conceptual and mechanistic synthesis figures,- robustness and recovery analyses,- translational feasibility visualizations,- and a supplementary simplified figure-generation script reproducing selected conceptual trends. The framework emphasizes:- energy-efficient adaptive control,- operable-window maintenance,- robustness under uncertainty,- recovery after perturbation,- and long-term adaptive scheduling. All figures and results are illustrative and simulation-derived. No real patient or clinical data are used. This work is intended for conceptual, methodological, and systems-level exploration of adaptive neuromodulation architectures and translational modeling frameworks.