What question did this study set out to answer?

This research aims to develop morphogenetic computing for self-organising neural network topologies using bioelectric voltage gradients.

May 8, 2026Open Access

Maya-Morphe: Bioelectric Gradient Fields as Computational Topology Primitives for Self-Organising Neural Architectures

Key Points

This research aims to develop morphogenetic computing for self-organising neural network topologies using bioelectric voltage gradients.
Systematic ablation with 36 trials across 3 conditions and 4 damage fractions to evaluate topology repair using bioelectric fields.
Comparison between voltage-driven spatial field repair and a fixed-topology baseline.
Evaluation of the Functional Recovery Rate (FRR) as the primary metric for assessing self-repair capabilities.
Voltage-driven spatial field repair achieved a 99.7% mean FRR with 12/12 full recovery across all damage fractions and seeds.
Fixed topology yielded an FRR of 0.0%, demonstrating its inability to self-repair.
The new Bhaya Quiescence Law peak modified to ~4.87% compared to the expected 0.32%.

Abstract

Maya-Morphe Paper 1 introduces morphogenetic computing — a new computational paradigm in which neural network topology self-organises via spatial bioelectric voltage gradient fields, inspired by Michael Levin's work on bioelectric tissue regeneration in planaria flatworms. We formalise Functional Recovery Rate (FRR) as the first evaluation metric for morphogenetic topology repair, and demonstrate through systematic ablation (3 conditions × 4 damage fractions × 3 seeds = 36 trials) that voltage-driven spatial field repair achieves 99. 7% mean FRR with 12/12 full recovery across all damage fractions and seeds. A fixed-topology baseline achieves 0. 0% FRR — it cannot self-repair by definition. The gap is 99. 7 percentage points. The Bhaya Quiescence Law is tested and reported as MODIFIED in this substrate (peak ~4. 87% vs 0. 32% expected). The Vairagya decay constant (0. 002315, ORCID-derived) is embedded in all canonical hyperparameters as a tamper-evident provenance signature. This paper establishes the evaluation framework and experimental protocol for the Maya-Morphe series. Series: Maya-Morphe Series 3, Paper 1ORCID: 0000-0002-3315-7907Canary: MayaNexusVS2026NLLBengaluruNarasimhaGitHub: https: //github. com/venky2099/Maya-Morphe-P1

Maya-Morphe: Bioelectric Gradient Fields as Computational Topology Primitives for Self-Organising Neural Architectures

Key Points

Abstract

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