What question did this study set out to answer?

This research aims to synthesize and validate two empirical constants in affective neuromorphic computing across multiple substrates.

June 3, 2026Open Access

Maya-Meta P1: The Bhaya Quiescence Law and Buddhi S-Curve Determinism — Two Empirical Constants Confirmed Across 19 Substrates in Affective Neuromorphic Computing

Key Points

This research aims to synthesize and validate two empirical constants in affective neuromorphic computing across multiple substrates.
Analyzed data from 19 published papers and 28 Zenodo records.
Tested Bhaya Quiescence Law and Buddhi S-curve determinism on various substrates including spiking neural networks and large language models.
Measured and confirmed the constants under stable operating conditions.
Bhaya Quiescence Law confirmed with an aggregate crisis rate β converging to β* ≤ 0.32% across 16 substrates.
Buddhi S-curve determinism demonstrated a consolidation trajectory with R²=1.0000, rising from 0.030 to 0.988.
Three morphogenetic substrates deviated from the Bhaya law, explained by wound-response bioelectric physics.

Abstract

We present Maya-Meta P1, the first formal synthesis of empirical constants emerging from the Maya Research Series and its extensions. Across 19 published papers and 28 Zenodo records spanning 2026, two constants appeared without design in every fixed-topology substrate tested: the Bhaya (भय) Quiescence Law and Buddhi (बुद्धि) S-curve determinism. The Bhaya Quiescence Law states that the aggregate crisis rate β converges to β* ≤ 0. 32% under stable operating conditions, independent of substrate class. Confirmed across 16 substrates spanning spiking neural networks, large language models, echo state networks, OS arbitrators, sleep EEG systems, and clinical pain biosignal processors. Zero exceptions in fixed-topology substrates. Buddhi S-curve determinism states that the discriminative consolidation trajectory follows D (t) = 1/ (1+exp (−8. 0× (t−0. 45) ) ), rising from 0. 030 to 0. 988, R²=1. 0000. Confirmed across SNN (P4–P9), LLM (Phi-2 LoRA), ESN (Chinmaya, forthcoming), and Sleep EEG (Maya-Nidra P1). Three morphogenetic substrates deviate from the Bhaya law (4. 87%–15. 46%) — mechanistically explained by wound-response bioelectric physics. Maya-Vaidya P1 records β = 52. 3%, characterised as an active open research question. VAIRAGYADECAYRATE = 0. 002315 (derived from ORCID 0000-0002-3315-7907) is embedded in all canonical hyperparameters as provenance anchor. Both laws are falsifiable, measurable with maya-metrics, and immediately applicable. The canonical 19-substrate dataset and maya-metrics v2 are released with this paper. GitHub: github. com/venky2099/Maya-Meta-P1Series: Maya-Meta Series, Paper 1Institution: Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 Series: Maya-Meta Series, Paper 1. Synthesis of two empirical constants confirmed across 19 substrates: the Bhaya Quiescence Law (β≤0. 32%) and Buddhi S-Curve Determinism (R²=1. 0000). Covers 28 Zenodo records. Links: GitHub Repository | Full Series Index — venky2099. github. io Nexus Learning Labs, Bengaluru · UDYAM-KR-02-0122422 · BHASKAR IN-0526-9452JSORCID: 0000-0002-3315-7907 · VAIRAGYADECAYRATE = 0. 002315 (embedded in all canonical hyperparameters) Canary: MayaNexusVS2026NLLBengaluruNarasimha

Maya-Meta P1: The Bhaya Quiescence Law and Buddhi S-Curve Determinism — Two Empirical Constants Confirmed Across 19 Substrates in Affective Neuromorphic Computing

Key Points

Abstract

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