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

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 (embedded in all canonical hyperparameters)Canary: MayaNexusVS2026NLL_Bengaluru_Narasimha