Maya-Morphe P3: Axon — Differentiable Bioelectric Edge Formation via Voltage-Guided Axonal Pathfinding in Morphogenetic Neural Networks

Version 2 (2026-08-04). Revised to the NLL Universal Paper Format v6. The original text is retained in full; nothing has been deleted. Corrections appear as marked blocks placed at the section that carries the claim, and each one states what the paper said, what the data show, the corrected claim, why it happened, and what still stands. This version withdraws the EFC interpretation and fixes a misattributed citation.EFC values of −0.009 to −0.019 are indistinguishable from zero, with no confidence interval, null distribution or permutation test, and per-epoch logs show single-run swings several times larger than the entire reported cross-scale range; P04 later reclassifies the same values as endpoint artefacts.Galvanotaxis was attributed in-text to "Bhattacharya et al. (2021)" six times; the bibliography entry is Mehta et al. (2021), Dev. Biol. 471:97–105, and every occurrence is corrected. The Gumbel-Softmax edge-formation implementation stands. Everything below this line is the original description from version 1. It is retained unchanged for the record. Where it conflicts with the corrections above, the corrections stand. Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2. Maya-Morphe P3: Axon extends the morphogenetic neural network framework developed in the earlier Maya-Morphe papers by asking a precise biological question: when a damaged cell recovers, does the specific pattern of connections it rebuilds affect tissue repair outcomes? To answer this, the authors introduce AxonNet, an architecture that adds a differentiable edge-formation module — using Gumbel-Softmax pathfinding — to guide recovering cells in forming new connections based on the surrounding bioelectric voltage field. Across 336 trials in Part 1 and 240 trials in Part 2, spanning cell populations from 29,056 to 266,071 cells, the study finds a scale-invariant revival threshold of approximately 0.162 and introduces Edge-Field Concordance (EFC) as the first quantitative metric for field-guided connectivity in morphogenetic computing, with EFC values ranging from −0.009 to −0.019 — indicating that recovering cells consistently probe slightly against the dominant voltage gradient in an exploratory manner. The threshold of ~0.162 is now confirmed across three independent experiments spanning all three Maya-Morphe papers, establishing it as an empirical substrate constant of this morphogenetic framework. This work is part of the Maya-Morphe Series from Nexus Learning Labs, Bengaluru, and advances the broader programme of using neuromorphic computation to model and simulate biological wound repair. Series: Part of the Maya-Morphe Series — morphogenetic neural networks for biological wound repair simulation. Bhaya Quiescence Law applies in modified form: dynamic-topology substrates show elevated β during active wound repair — biologically predicted and validated. Links: GitHub Repository (private — to request access: email research@nexuslearninglabs.in with subject Code Access Request — Maya-Morphe-P3 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