Maya-Morphe P4: Dharana — EFC Trajectory Dynamics 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 theory-confirmation claim.The 50k trough rests on n = 1 per scale with no error bars, is non-monotone across five points, and two of those points resolve to the same 266,071-cell grid, so it cannot confirm Manicka & Levin's optimal-sensitivity prediction.The measurement-collapse diagnosis is the paper's real contribution and is stronger than stated — compute_efc returns literal 0.0 when score variance falls below threshold, so EFC = 0.000 at epoch 250 is a degenerate-variance guard firing. This paper's supersession of P02's constant and P03's EFC values is now made explicit. 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 Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2.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 P4: Dharana investigates how Edge-Field Concordance (EFC) — a measure of alignment between network connectivity and emergent field activity — evolves over training time in morphogenetic neural networks designed to simulate biological wound repair, building on the static EFC snapshot reported in P3. Rather than treating EFC as a fixed endpoint, this work tracks its full trajectory across 250 training epochs to understand how these networks reorganize themselves dynamically. The central finding is that EFC follows a universal U-shaped trajectory across scales, beginning near zero, descending into negative territory (reaching values between -0.009 and -0.019 as previously reported in P3), and recovering — a pattern interpreted as a structured phase of exploratory edge formation followed by consolidation. This is the fourth entry in the Maya-Morphe Series, which applies morphogenetic principles to neural network design for wound repair simulation, and results here reinforce the modified Bhaya Quiescence Law established across the series: dynamic-topology substrates exhibit elevated β during active repair phases, a behavior that is both biologically predicted and empirically confirmed. Together, P3 and P4 reframe EFC not as a diagnostic snapshot but as a dynamic signature of how morphogenetic networks learn. 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-P4 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