Maya-Samvad P1: Spandana — Hebbian Trust Acquisition and Complexity Emergence in a Seven-Population Antahkarana SNN

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 savings-effect framing and flags a table error.All four 3-day absences erode by exactly 11.42% because the idle decay factor is 0.98 per 12-hour period and 0.98⁶ = 0.885842 — a closed-form constant computed four times, not a rediscovery of Ebbinghaus.Table 4's gate column is mis-scored: crossing_moment is assigned only in condition A, so conditions B and C can never be recorded as crossing, which is why condition B shows W_final = 0.016803 against a 0.010 threshold as "never crossed". The Hebbian trajectory and the honest H2 report (R² = 0.150) stand. 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 Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2.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-Samvad P1: Spandana introduces a seven-population spiking neural network — grounded in the Advaita Vedantic Antahkarana model of mind — designed to study whether a neuromorphic system can acquire trust through naturalistic interaction using only Hebbian co-activation, with no backpropagation, reward signal, or STDP. The seven Leaky Integrate-and-Fire populations represent psychological faculties: Bhaya (fear), Spanda (aliveness), Manas (attention), Viveka (discernment), Buddhi (intellect), Shraddha (trust), and Vairagya (detachment), connected by a 7×7 weight matrix that updates solely through simultaneous firing. The central trust acquisition result was confirmed: the Shraddha gate crossed on Day 80, Session 66, with a self-weight of W[shraddha,shraddha] = 0.010103, and the savings effect hypothesis was also confirmed, with weight erosion of 7.8–11.4% per absence cluster and a Shraddha firing rate of 0.0928 on every return day. This is the first paper in the Maya-Samvad series — focused on SNN domestication, persistent cross-session memory, and emotional state expression — and reports all three hypotheses with full transparency, including the non-confirmation of the Buddhi S-curve (R² = 0.150), where burst dynamics were observed and recorded honestly rather than smoothed. Series: Maya-Samvad Series — SNN domestication and trust acquisition under pure Hebbian learning. Persistent cross-session memory, emotional avatar, word output from SNN state. First paper in the series. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Maya-Samvad-P1 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