Adaptive Biohybrid Neural Interface (ABNI): A Computational Feasibility Framework for Closed-Loop Neuromodulation, Thermal Safety, and System-Level Engineering Sub-Models

Abstract

This paper presents the Adaptive Biohybrid Neural Interface (ABNI) as a computational feasibility framework: a set of independently verifiable mathematical and computational sub-models for the major subsystems such an interface would require, rather than an integrated, biologically validated device. Its core contribution is a constrained reinforcement learning (RL) closed loop pulsed neurostimulation controller, arrived at after two findings obtained by direct simulation necessitated a redesign of the initial approach: first, constant current stimulation of the underlying two compartment Hodgkin Huxley (HH) neuron model cannot reach a 10 Hz target firing rate, because of the model's discontinuous Type II excitability, so pulsed stimulation is used instead, with one to one pulse frequency to firing rate entrainment verified over 0 to 80 Hz; and second, an initial, additively combined RL reward collapses to a degenerate zero stimulation policy (reward hacking), motivating a Lagrangian relaxation constrained MDP reformulation. The resulting controller outperforms continuous, on off, and PID baselines on tracking error under identical conditions while using 7.7 percent of a continuous 130 Hz clinical deep brain stimulation baseline's average frequency. We separately derive and cross validate a closed form Pennes bioheat and photon diffusion thermal safety threshold (q0,thresh equal to 3.38 times 10 to the fourth watts per cubic meter) with a Sobol sensitivity analysis, validated against an analytic ground truth, identifying blood perfusion as the dominant uncertainty driver. Beyond this validated core, computational sub-models are provided for the remaining engineering subsystems an ABNI type device would require: cell device adhesion mechanics (a Bell/Evans dynamic force spectroscopy model cross validated against an independent stochastic simulation, 0.28 percent agreement), macrophage M1/M2 polarization dynamics (a compartmental ODE whose sensitivity is properly evaluated at a finite, clinically relevant time point rather than at the trivially saturated infinite time limit), an on chip spiking neural network rate decoder (a benchmarked leaky integrate and fire population; software latency only, not compared to neuromorphic hardware figures), a lightweight ARX block cipher implementation for telemetry security (verified for round trip correctness and avalanche diffusion; independent verification against officially published test vectors is recommended before any security critical deployment), an information theoretic analysis of the bidirectional recording channel using inter spike interval entropy at the controller's native 1 ms operating resolution, and first order bioresorption kinetics for transient electronics, which correctly preserve strict positivity of the degrading structure at all times, in contrast to a zero order approximation. Every quantitative sub-model result in this paper is either analytically derived, cross validated against an independent method, or explicitly identified as depending on an illustrative, rather than empirically measured, parameter; none is presented as clinically or experimentally validated, and no biological, animal, or human data of any kind were used. Three further sub-models complete the system level picture: an electrode electrolyte interface analysis, which shows that a stimulation current representative of conventional microelectrodes is incompatible with a subcellular electrode footprint under standard double layer capacitance assumptions and derives the correspondingly small safe operating current; a photovoltaic energy harvesting model with Perturb and Observe maximum power point tracking, which sustains 99.5 percent of the true maximum extractable power under a representative fluence disturbance; and Langmuir protein adsorption kinetics, which establishes a minutes scale precursor timescale to the days scale cellular immune response. A derived system latency budget further shows that firing rate sensing, not on chip processing, accounts for over 99 percent of total closed loop latency at the target operating frequency.