Maya-Morphe P2: Spanda — Adaptive Bioelectric Topology Learning via Gradient-Guided Voltage Thresholds and Dynamic Edge Formation

Maya-Morphe P2: Spanda (स्पन्द — the first pulse) extends the morphogenetic computing framework from Paper 1 to a multi-scale progressive study across 7 grid sizes, introducing learnable voltage revival thresholds via GraphSAGE-based spatial field encoding. Paper 1 asked: can voltage-driven repair beat fixed-topology? Answer: yes, 99. 7% vs 0. 0% FRR. Paper 2 asks: what is the optimal voltage threshold — and does it change with scale? SpandaNet replaces the fixed threshold (0. 18) with an nn. Parameter trained via a differentiable soft FRR objective. Through 252 trials across 7 scales (29, 056 to 266, 071 actual cells), gradient descent consistently discovers ~0. 163 — approximately 0. 017 below Paper 1's hardcoded value. This finding is scale-invariant. FRR = 100% for both learned and fixed conditions. Fixed-topology: 0. 0%. The Bhaya Quiescence Law is MODIFIED at ~15. 4% across all 7 scales — scale-invariant and consistent with Paper 1. Vairagya decay constant (0. 002315, ORCID-derived) is embedded in all model parameters. Series: Maya-Morphe Series 3, Paper 2ORCID: 0000-0002-3315-7907Canary: MayaNexusVS2026NLLBengaluruNarasimhaGitHub: https: //github. com/venky2099/Maya-Morphe-P2Paper 1 DOI: 10. 5281/zenodo. 20052072