Maya-Vaidya P2: A Four-Head SoftLIF Spiking Neural Network for Wearable Pain Chronification Staging with Buddhi S-Curve Classification and Adaptive BCM Vairagya

Version 2 (2026-08-04). Revised to the NLL Universal Paper Format v6. The original text is retained in full; nothing has been deleted. Corrections appear as marked blocks placed at the section that carries the claim, and each one states what the paper said, what the data show, the corrected claim, why it happened, and what still stands. This version corrects the headline comparison and the biomarker claim."+29% above chance (0.333)" is measured against the wrong reference: the majority-class baseline is 0.4524 and appears in the paper's own table, so the model performs worse than always predicting Stage 1; and the frozen BCM chronification signal reduces algebraically to GEV × 0.002315 = 0.001709, exactly the reported 0.0017.The permutation test (p = 0.068, correctly reported as non-significant) and the label-circularity limitation stand, both of which I raised myself. Everything below this line is the original description from version 1. It is retained unchanged for the record. Where it conflicts with the corrections above, the corrections stand. Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2. Maya-Vaidya P2 presents a four-head Parallel SoftLIF Spiking Neural Network trained on raw wearable biosignals — electrodermal activity, blood volume pulse, accelerometer, and skin temperature from an Empatica E4 wristband — to predict pain chronification stage without any survey input at inference time, addressing a critical gap in chronic pain assessment for India's approximately 190 million sufferers. The system introduces an AdaptiveChronificationTracker using a frozen BCM synaptic rule, which receives no gradient supervision from any training head yet produces signal patterns that trend toward distinguishing chronic from acute pain stages. On the PhysioPain dataset under Leave-One-Subject-Out cross-validation across 84 subjects, the Buddhi stage classification head achieved 0.4302 accuracy — 29% above the 0.333 chance baseline — with a permutation test yielding p=0.068, a trend toward significance at this sample size. This result sits within the Maya-Vaidya Series and confirms the canonical Buddhi S-curve parameters (k=8.0, t₀=0.45) consistent with findings across 14 or more prior Maya substrates, reinforcing their empirical stability as a cross-substrate labelling standard. Maya-Vaidya P2 is an India-first neuromorphic research prototype, designed for clinical pain analysis in resource-constrained primary care settings, and is not a licensed medical device. 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-P2 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 — an ORCID-derived provenance mark, not an experimental parameter. Each paper's disclosure block states whether it reached a result in that paper.Canary: MayaNexusVS2026NLL_Bengaluru