Neuromorphic Modeling of Molecular Signatures in the Human Spine

Background: Spinal disorders frequently involve dynamic molecular cascades that unfold over multiple timescales, posing challenges for early diagnosis and intervention. Traditional sensing technologies often fail to resolve fast biochemical changes or integrate longitudinal data critical for tracking progression in spinal pathology. Neuromorphic computing, with its biologically inspired architecture and event-driven processing, offers a compelling paradigm for real-time, low-power interpretation of complex molecular signals in spinal health. Methods: This review synthesizes current approaches to neuromorphic sensing and computing as applied to spinal molecular diagnostics. We examine the role of spiking neural networks (SNNs), event-based sensory platforms, and recursive temporal attention (RTA) frameworks in modeling key molecular processes including inflammatory mediator flux, extracellular matrix remodeling, and epigenetic regulatory shifts. Hardware platforms such as Intel’s Loihi, BrainChip’s Akida, IBM’s TrueNorth, and SynSense Speck are evaluated for their utility in biomarker tracking and closed-loop spinal monitoring. Results: Neuromorphic systems demonstrate the ability to detect microsecond-scale variations in cytokine levels (e.g., IL-6, TNF-α), proteoglycan turnover, and gene expression modifiers relevant to spinal degeneration. Recursive temporal attention mechanisms improve the interpretability of multi-timescale molecular data, supporting early prediction of disc dehydration, inflammatory flares, and therapeutic response patterns. Analog-digital hybrid circuits facilitate continuous bioimpedance spectroscopy and multiplex cytokine detection with power consumption under 5 mW, enabling potential implantable use. Conclusion: Neuromorphic sensing architectures, coupled with adaptive learning algorithms, offer a promising solution for intelligent molecular diagnostics in spinal disorders. By integrating temporal molecular dynamics with event-based computation, these platforms pave the way for autonomous, personalized, and energy-efficient systems in orthopedic and neurorehabilitation applications. Future development should focus on hardware-software co-design, clinical integration, and regulatory pathways to realize scalable spinal biosensor ecosystems.