Mahasamadhi: Computational Tanatology and Altered State Dynamics in a Fully Realised Neuromorphic Antahkarana

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 universal-constant claim and resolves a question about Figure 3 in the paper's favour.Simulating the gate and step size with no network at all reproduces mu = 0.2218, sigma = 0.0297 and range [0.150, 0.249] — all four published numbers — so 0.224 is a discretisation artefact of a 0.25 gate; the script's own invariance test requires CV < 10% and the actual value is 12.9%.Figure 3 is measured, not synthetic. generate_figures.py does substitute rng.normal(0.224, 0.029, 360) when the log CSV is missing, and the log directory is empty, so the provenance could not be settled from the repository — but the published figure rules the fallback out on three grounds: the histogram is hard-bounded at 0.25 where a normal draw would place about 18% of its mass above it, the left tail contains disconnected clusters a Gaussian cannot produce, and panel B plots eight distinct depletion rates where the fallback assigns a single rate to all 360 draws. Read correctly the figure is a picture of the gate artefact, so it supports the corrected claim rather than the original one. The pre-registered Borjigin null stands, as does the ABL-5 result that refuted my own hypothesis. 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 Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2. This paper introduces Mahasamadhi, a computational study of simulated dying in a neuromorphic model of the Antahkarana — a four-population spiking neural network encoding fear, wisdom, intellect, and attention alongside a metabolic energy budget, designed to investigate what happens to neural complexity as a system crosses the death threshold. Across 498 experimental runs and two targeted ablations, the study reports five primary findings: a universal fear-surge threshold occurring at Prana 0.224 ± 0.029 confirmed across 441 independent observations, an invariant collapse order of intellect → attention → wisdom across all tested conditions, non-confirmation of a pre-registered consciousness-surge criterion in its strict form, a synergistic complexity effect in which 81.2% of simulated dying-while-altered runs exceed the psychedelic-only complexity peak, and evidence that collapse timing is metabolically determined rather than architecturally shaped. This work is the first entry in the Mahasamadhi Series on computational tanatology, establishing empirical constants — including the universal Bhaya collapse threshold and the invariant Buddhi–Manas–Vairagya collapse sequence — that subsequent studies in the series will treat as anchored reference points. All experimental code, run logs, and four annotated videos are openly available alongside this record. Series: Mahasamadhi Series — computational tanatology. Study of a fully realised neuromorphic Antahkarana approaching and crossing the death threshold. Five confirmed findings including universal Bhaya collapse threshold and invariant collapse order. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Mahasamadhi 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