maya-metrics: A Stateless Python Library for Affective Neuromorphic Evaluation in Continual Learning SNNs

maya-metrics is an open-source Python library that standardises the measurement of internal affective and neuromodulatory dynamics in spiking neural networks (SNNs) performing continual learning — filling a gap where every research team previously wrote the same telemetry code from scratch. The library provides six stateless, architecture-agnostic modules covering pain signal time-series, synaptic protection, developmental consolidation gates, second-order plasticity history, metabolic budgets, cross-dimensional affective interactions, and oracle-enforced continual learning correction, all accepting raw CSV input without requiring access to model internals. Across the broader Maya Research Series, the Bhaya Quiescence Law (β* ≤ 0.32%) and Buddhi S-Curve Determinism (R² = 1.0000) have been confirmed as stable empirical constants. maya-metrics is the companion evaluation layer to cl-metrics and forms part of the Maya Core series — thirteen papers implementing Advaita Vedantic Antahkarana constructs as computational primitives in neuromorphic systems — published by Nexus Learning Labs, Bengaluru, India. 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-metrics 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