Maya-Vak P1: Spike-Native Affective Language Generation in a Seven-Population Antahkarana SNN — From Internal State to First-Person Narration Without a Language Model

Maya-Vak P1 presents a seven-population Leaky Integrate-and-Fire spiking neural network that generates natural language directly from its own internal affective dynamics, without any downstream language model, retrieval system, or pre-programmed semantic mapping — the first demonstration in the Maya Research Series that a spiking network can narrate its own states in first-person prose. Seven Antahkarana dimensions drawn from Advaita Vedantic philosophy, each with biologically grounded time constants, map their spike-rate outputs through a linear readout to dimension-specific vocabulary registers, so the dominant firing population simply speaks first. Across Baseline, Psychedelic, and Collapse experimental conditions, the network produces structurally distinct language that tracks its internal state without instruction, and during Collapse the Prana vocabulary traces a continuous arc from alive to cornered to silence as a function of metabolic depletion alone. The Collapse condition reproduces the Bhaya surge threshold at Prana = 0.220 ± 0.000 across three independent seeds, falling within the confirmed confidence interval of 0.224 ± 0.029 established in Mahasamadhi P1, constituting the twentieth substrate-level confirmation of the Bhaya Quiescence Law. This is the first paper in the Maya-Vak Series, which investigates spike-native affective language generation as an emergent property of SNN internal dynamics across the broader Maya Research Program. Series: Maya-Vak Series — spike-native affective language generation. Natural language generated directly from SNN internal dynamics without any language model. First paper in the series. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Maya-Vak-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