Maya-Vaidya P4: Sphurana — Measuring the Computational Libet Gap in a Seven-Population Antahkarana Spiking Neural Network Under Nociceptive Stimulation

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 withdraws the Libet-gap interpretation.Δ > 0 is true by construction — the fast path fires at a membrane threshold of 0.045 while the slow path needs at least 22 ticks of 0.02 accumulation, both constants verified in code — so no run could return Δ ≤ 0; and ablating Spanda yields Δ = None because T_action is defined as Spanda crossing, which is a definition rather than a mechanism test.The seven-population implementation stands. Effective n is 9–11 rather than 33 (the paper concedes std = 0.0), H2 fails at p = 0.065, and Δ is in ticks, so no comparison to Libet's 550 ms is possible. 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 Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2.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 P4: Sphurana introduces a seven-population spiking neural network built on an Advaita Vedantic cognitive architecture to formalise and measure the Computational Libet Gap — the temporal interval between a network's motor output and its deliberative cognitive resolution — under simulated nociceptive (pain) stimulation. Drawing on Benjamin Libet's readiness potential experiments and the biology of spinal reflex arcs, the study asks whether a neuromorphic system can act before it decides, and whether that gap can be quantified as a reproducible metric. The central hypothesis is confirmed: under acute, chronic, and ambiguous pain conditions the Computational Libet Gap (Δ = T_decision − T_action) measures 49 ms, 29 ms, and 32 ms respectively, with ablation of the Spanda population collapsing the gap entirely and identifying it as the necessary mechanism of pre-conscious motor initiation. This is the fourth paper in the Maya-Vaidya Series and confirms the Buddhi sigmoid consistency property (D(t) = 1/(1+exp(−8.0×(t−0.45))), R² = 0.9999) across all experimental conditions, a formula-determined result documented in the series meta-protocol Maya-Meta P2. Maya-Vaidya is an India-first neuromorphic research programme targeting clinical pain analysis, and this work is a research prototype only — it 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-P4 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