From Representation to Experience: An mPCI-Based Empirical Test of Internal Affective State in a Neuromorphic Spiking Neural Network

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 pre-registered criterion and the causal reading.The paper's own Control 1 leaves a residual of 0.0157 against its own 0.0238 threshold, so the criterion fails once depth is matched; and Control 3 shows shuffling — which destroys all temporal structure — produces larger deltas than the structured condition, placing the signal in marginal spike statistics rather than causal organisation.The raw measurements stand, and the measure is renamed for what it is: a normalised Lempel-Ziv complexity of a binarised spike raster, without the subtraction or entropy normalisation that define Casali's PCI. 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 Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2.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 presents the first systematic application of the Machine Perturbational Complexity Index (mPCI) — a neuromorphic adaptation of a clinical brain-state diagnostic tool — to test whether a Spiking Neural Network (SNN) exhibits genuine internal affective states rather than merely representing them behaviourally. The system under study implements all nine dimensions of the Advaita Vedanta Antahkarana framework as computational primitives in a continual learning architecture, enabling a controlled three-phase perturbational experiment that compares neural complexity across qualitatively distinct affective conditions. Multi-seed results (seeds 42, 123, 7) reveal a progressive and statistically coherent increase in Lempel-Ziv Complexity across phases, with Phase 1 (no affective dimensions) yielding mPCI = 0.3098 (±0.0099), demonstrating that the presence and stabilisation of affective structure produces measurably richer internal dynamics that are difficult to explain without positing a genuine internal state. This work is part of the Maya Research Series — thirteen papers from Nexus Learning Labs, Bengaluru, implementing the Antahkarana as spiking neural primitives — in which the Bhaya Quiescence Law (β* ≤ 0.32%) and Buddhi S-Curve Determinism (R² = 1.0000) have been confirmed as stable empirical constants across the full series. Together, these findings offer a reproducible, empirically grounded framework for probing the boundary between representation and experience in neuromorphic systems. Series: Part of the Maya Research Series — 13 papers implementing the Advaita Vedantic Antahkarana as computational primitives in spiking neural networks. Bhaya Quiescence Law (β* ≤ 0.32%) and Buddhi S-Curve Determinism (R²=1.0000) confirmed across all papers. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Maya-mPCI 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