Digital Neurophysiology of Sleep: Explainable Machine Learning Frameworks for Modeling Proton-Driven Brain Homeostasis and Sleep Regulation

Abstract

Sleep regulation is increasingly understood as a process driven not only by neural circuits but also by fundamental bioenergetic and ionic signals, among which proton dynamics have emerged as a primary homeostatic driver. Recent work using genetically encoded sensors has directly demonstrated that protons accumulate during wakefulness and serve as a molecular trigger for sleep onset, reframing our understanding of the sleep-wake cycle. This paper proposes an integrated framework that combines explainable machine learning architectures with digital neurophysiology to model proton-driven brain homeostasis and sleep regulation at scale. We examine the structural trade-offs between deep learning interpretability and predictive accuracy in modeling complex ionic-stress feedback loops, and we design a system-level infrastructure for deploying such models across distributed clinical and research environments. Key considerations include the robustness of proton-sensor data pipelines, the fairness of sleep monitoring systems when applied to diverse populations, and the governance challenges introduced by continuous neural state estimation. Cross-domain comparisons with immune gene typing and automated clinical trial validation platforms illustrate common architectural patterns. The paper concludes with a perspective on sustainable deployment and policy implications for next-generation sleep health informatics.