Maya-Vaidya P1: A Neuromorphic Dual-Head Spiking Neural Network for Multimodal Pain Classification and Severity Estimation with BCM Chronification Tracking

Maya-Vaidya P1 introduces a dual-head spiking neural network (SNN) designed to simultaneously classify pain type and estimate pain severity from physiological signals, addressing the need for neurobiologically grounded computational models of clinical pain that can generalise across individuals. The architecture combines leaky integrate-and-fire (LIF) neurons with a BCM ChronificationTracker — a novel module that models the acute-to-chronic pain transition through a sliding modification threshold — trained and evaluated using rigorous Leave-One-Subject-Out cross-validation across 86 participants drawn from the PhysioPain dataset. Across all 86 independent LOSO folds, the model achieved a cross-subject GEV (Global Excitatory Voltage) convergence of 0.3693 ± 0.0197 — invariant across subjects, pain types, and all five ablation conditions — alongside 4-class pain classification accuracy of 43.0% (macro F1 = 0.371), headache detection F1 = 0.576, and pain severity MAE = 0.759 on a 0–5 Likert scale, outperforming an MLP baseline. The GEV invariance replicates the Bhaya Quiescence Law confirmed across 14 or more prior Maya substrates, and the embedded decay constant PAIN_DECAY_RATE = 0.002315 serves as a canonical IP anchor across both loss weighting and membrane dynamics. As the first entry in the Maya-Vaidya series, this work advances India-first neuromorphic AI for clinical pain analysis, offering an open research prototype toward accessible, physiologically interpretable pain assessment tools. This is a research prototype only and is not a licensed medical device; for medical emergencies in India, please contact 112. Series: Part of the Maya-Vaidya Series — neuromorphic AI for clinical pain analysis. India-first. Research prototype only — NOT a licensed medical device. For medical emergencies in India: 112. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Maya-Vaidya-P1 and your research context) | Interactive Dashboard | FAQ | Full Series Index — venky2099.github.io Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 · BHASKAR IN-0526-9452JSORCID: 0000-0002-3315-7907 · VAIRAGYA_DECAY_RATE = 0.002315 (embedded in all canonical hyperparameters)Canary: MayaNexusVS2026NLL_Bengaluru_Narasimha