Maya-Morphe P5: Dhyana — Sustained Field-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 field-guided pathfinding claim.EFC is an explicit optimisation target fed by its own output: the edge loss directly rewards the correlation EFC measures, the diversity loss manufactures the field gradient EFC reads, and the EMA multiplies the candidate field before scoring — all three couplings are quoted from the code.Driving the metric positive confirms the optimiser works, not that pathfinding occurred; the decay back to −0.012 by epoch 250 was honestly reported and stands, as does the engineering description. 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 Two framings it repeats have since been withdrawn in full — the Bhaya Quiescence Law and the Buddhi S-Curve. Both are addressed in Maya-Meta P1, now superseded, and in the self-audit of Maya-Meta P2.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 P5: Dhyana introduces the first demonstration of stable field-guided axonal pathfinding in a morphogenetic neural network, a system designed to simulate how biological tissue repairs itself by regrowing nerve connections across a wound. Building on Paper 4 (Dharana), which revealed that apparent field convergence to zero was a measurement artefact of near-deterministic sampling rather than a genuine substrate property, P5 introduces three targeted training interventions — a wound-aware field diversity loss, an FRR loss freeze triggered once recovery stabilises, and a per-epoch scalar EMA field amplifier implementing the Manicka-Levin slow field mechanism — each addressing a specific failure mode identified in P4. At 50,000-cell scale (144,726 cells), the Edge Formation Coefficient (EFC) is positive in 96 of 250 training epochs, with a peak EFC of +0.1565, marking the first confirmed instance of the network actively preferring wound-directed axonal connections over random ones. This result validates the Bhaya Quiescence Law in its modified form for dynamic-topology substrates, confirming the empirically established prediction that elevated β during active wound repair is a biological feature rather than an anomaly. P5: Dhyana is the fifth paper in the Maya-Morphe Series from Nexus Learning Labs, Bengaluru, India, advancing a research programme in morphogenetic neural computation aimed at biologically grounded models of tissue-level neural regeneration. 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-P5 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