What question did this study set out to answer?

To develop and validate maya-metrics, an open-source library for assessing affective dynamics in neuromorphic networks during continual learning.

April 15, 2026Open Access

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

Puntos clave

To develop and validate maya-metrics, an open-source library for assessing affective dynamics in neuromorphic networks during continual learning.
Developed six stateless modules: AffectiveMetrics, CrossDimensional, ComplexityMetrics, MaturationIndex, CrossSubstrate, and CLCorrector.
Validated against 11 published studies across two substrates: spiking neural networks and large language models.
Conducted a unit test with 16/16 passing successfully.
Confirmed two constants: Bhaya Quiescence Law and Buddhi S-curve determinism.
Demonstrated the effectiveness of the library for evaluating class-incremental learning in SNNs.

Resumen

maya-metrics is the first open-source Python library for evaluating internal affective and neuromodulatory dynamics in neuromorphic spiking neural networks (SNNs) engaged in class-incremental continual learning (CIL). Six stateless modules: AffectiveMetrics, CrossDimensional, ComplexityMetrics, MaturationIndex, CrossSubstrate, and CLCorrector. Validated against 11 published preprints across two substrates (SNN + LLM) and one embodied PiCar-X deployment. 16/16 unit tests pass. Confirms two series constants programmatically: Bhaya Quiescence Law and Buddhi S-curve determinism. KARMADECAYRATE = 0. 002315.

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