Neuromorphic Modeling of Molecular Signatures in the Human Spine
Rahul Kumar 1, Kyle Sporn 2, Puja Ravi 3, Akshay Khanna 4, Nasif Zaman 5 and Alireza Tavakkoli 5* 1 Department of Biochemistry and Molecular Biology, University of Miami Miller School of Medicine, Miami, Florida, United States 2 Upstate Medical University Norton College of Medicine, Syracuse, New York, United States 3 University of Michigan, Ann Arbor, Michigan, United States 4 Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, Pennsylvania, United States 5 Human-Machine Perception Laboratory, Department of Computer Science, University of Nevada Reno, Reno, Nevada, United States * Correspondence:
[email protected]
Abstract: 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 multitimescale 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 codesign, clinical integration, and regulatory pathways to realize scalable spinal biosensor ecosystems...
While the Akida chip is commercially used in edge medical devices and supports on-device learning, allowing it to adapt to patient-specific molecular patterns without requiring cloud connectivity, it has shown that it can rapidly diagnose with biosensors [5]. Leveraging its massively parallel, event-driven design to identify subtle patterns and correlations that might indicate early pathogenic changes in spinal tissues,...
5. Garcia-Palencia, O.; Fernandez, J.; Shim, V.; Kasabov, N.K.; Wang, A.; the Alzheimer’s Disease Neuroimaging Initiative. Spiking Neural Networks for Multimodal Neuroimaging: A Comprehensive Review of Current Trends and the NeuCube Brain-Inspired Architecture.
https://doi.org/10.3390/bioengineering12060628 Bioengineering 2025, 12, 628.
The breadth of use cases for AKIDA technology just keeps being revealed.
My opinion only DYOR
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