Conceptual Framework for Adaptive Biohybrid Neural Interfaces: Innovations in Medical Neural Engineering to Address Biocompatibility and Energy Efficiency Challenges

This conceptual paper proposes an adaptive biohybrid neural interface (ABNI) framework that integrates living immune cells (monocytes) with subcellular-sized wireless photovoltaic electronic devices to enable minimally invasive, targeted closed-loop neuromodulation in inflamed brain regions. Drawing from recent advances in biohybrid technologies, the framework addresses key challenges in neural engineering, including biocompatibility, energy efficiency, and chronic implant stability. We present rigorous mathematical models with detailed derivations, Python-based simulations using replicable real-world data, advanced sensitivity analysis, quantitative statistics, Bayesian inference, uncertainty quantification, and falsifiability assessments. Supported by high-fidelity TikZ diagrams, comparative tables, and citations from prestigious peer-reviewed journals, this work outlines a multi-phase roadmap from experimentation to manufacturing, emphasizing ethical considerations and self-contained data availability.