A Neuroengineering Load–Capacity Control Model for Predicting Neural Instability Transitions

Sue C. CacicedoUCL Medical Physics and Biomedical EngineeringORCID: 0009‑0006‑4811‑1115 This work introduces a neuroengineering load‑capacity control model for predicting transitions from stable to unstable neural states in vestibular and multisensory disorders. Instability is formalized as a dynamical phenomenon emerging when cumulative load L(t) exceeds regulatory capacity K(t), expressed through the control parameter \Phi (t)=L(t)/K(t). The model integrates sensory, cognitive, affective, metabolic, and environmental domains into a unified mathematical architecture, defining stability margins, precision collapse, and entropy growth as emergent properties of the system. Through state‑space formulation, linearization, and numerical simulation, the framework reproduces three characteristic regimes—stable, transitional, and unstable—mirroring clinical patterns observed in chronic vestibular conditions such as PPPD and MdDS. Beyond its mathematical contribution, the work addresses a longstanding gap in the field: the absence of a mechanistic, quantitative ontology capable of integrating multisensory, cognitive, autonomic, and environmental processes. Existing theories remain fragmented, relying on descriptive or domain‑specific heuristics. This model reframes instability as a systems‑level transition governed by load‑capacity interactions, offering a computationally testable structure for future empirical validation and potential clinical decision‑support applications. AcknowledgementsI acknowledge the kind of support that has become increasingly rare in contemporary society — a network capable of sustaining transformation without resistance, fragmentation, or emotional cost. In a world where time is scarce, responsibilities multiply, and relational structures often collapse under pressure, I was met with alignment. That is exceptional. Anthropology reminds us that genuine support networks are not defined by proximity, but by their ability to reorganize around change. Throughout this demanding period, marked by expanded clinical responsibilities, academic leadership, and sustained intellectual work, the people closest to me adapted with resilience rather than friction. Their steadiness made it possible for growth to occur without compromising continuity. This work reflects a collective that absorbed impact, redistributed load, and allowed transformation to unfold with coherence. I reached this moment because those around me moved with me, not against me. My gratitude is complete.