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 (SWED) 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, including cell-device adhesion mechanics accounting for microfluidic drag forces, advanced optical link budget with scattering and maximum power point tracking (MPPT) for energy stability, electrode-electrolyte interface modeling, immunological dynamics, multi-compartment neural models, spiking neural network (SNN) models for on-chip processing, system latency budget, bidirectional brain-computer interface (BCI) capabilities, and end-of-life strategies. Python-based simulations using replicable real-world data incorporate safe reinforcement learning, advanced sensitivity analysis (including expanded Sobol indices with first-order, higher-order, and total-effect decompositions), quantitative statistics, Bayesian inference, uncertainty quantification, and falsifiability assessments. Supported by high-fidelity TikZ diagrams, comparative tables, failure mode analysis, and citations from prestigious peer-reviewed journals, this work outlines applications in neurology, a multi-phase roadmap from experimentation to manufacturing, emphasizing ethical considerations, nanofabrication feasibility, neural cybersecurity, and self-contained data availability.