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

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 (embedded in all canonical hyperparameters)Canary: MayaNexusVS2026NLL_Bengaluru_Narasimha