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: MayaNexusVS2026NLL_Bengaluru_NarasimhaGitHub: https://github.com/venky2099/Maya-Morphe-P2Paper 1 DOI: 10.5281/zenodo.20052072