Mycelial Intelligence Framework · Open Science
There is a brain beneath every forest. FUNGI-MYCEL makes it visible.
FUNGI-MYCEL introduces the first mathematically rigorous, AI-integrated multi-parameter framework for the quantitative characterization of mycelial network intelligence — the Mycelial Network Intelligence Score (MNIS).
"Mycelium is not merely a collection of threads — it is a distributed computational substrate, a living intelligence that processes environmental data through bioelectrical spike trains propagating at 0.5–5 mm/second across networks spanning hectares."
The framework elevates the study of fungal networks from descriptive mycology to rigorous systems science — providing the measurement tools to decode this intelligence across sites, seasons, and disturbance regimes.
Eight physically independent parameters, combined into the composite MNIS score.
Rate of mineral dissolution per unit hyphal surface area — the geochemical foundation of mycelial productivity.
Frequency and structure of electrical spike trains per network node — the language of mycelial intelligence.
Directional accuracy of hyphal tip navigation toward resource targets — verified within ±8° of optimal trajectory.
Fidelity of host-fungal nutrient transactions to predicted optimal stoichiometry across 87 paired interfaces.
Fractal expansion coefficient encoding the ecosystem's carbon sequestration efficiency — r = +0.917 with bioelectrical density.
Rhizospheric biodiversity amplification ratio relative to bulk soil — mean 1.84× across all intact sites.
Post-disturbance network recovery half-time — modeled via B(t) = B_max·(1−e^(−t/τ)) with τ½ = 4.1 ± 0.7 years.
Network response plasticity under environmental stress gradients — captured via critical slowing-down signatures.
A single dimensionless number encoding the functional state of a living network with sufficient precision to guide intervention and forecast ecological outcomes.
// Mycelial Network Intelligence Score // FUNGI-MYCEL Composite Formula MNIS = 0.20 · η_NW // Mineral Weathering Efficiency + 0.20 · ρ_e // Bioelectrical Pulse Density + 0.15 · ∇C // Chemotropic Navigation Gradient + 0.15 · SER // Symbiotic Exchange Ratio + 0.12 · K_topo // Topological Fractal Dimension + 0.08 · ABI // Adaptive Biodiversity Index + 0.05 · BFS // Biological Field Stability + 0.05 · ARC // Adaptive Resilience Coefficient // Sigmoid correction for non-linear interactions: MNIS_final = σ(Σ wᵢ·xᵢ + β) // where σ(z) = 1 / (1 + e⁻ᶻ)
The largest validated dataset on mycelial network intelligence ever assembled — spanning five biomes and two decades of continuous observation.
MNIS prediction accuracy exceeds 90% across all five monitored biome types — validated via leave-one-site cross-validation across 39 sites.
ρ_e × K_topo correlation r > 0.90 — bioelectrical density encodes network topology, confirmed via microelectrode recordings vs. confocal fractal dimension.
η_NW mineral weathering rate varies by >10× between intact and degraded networks — tested via ICP-MS at 156 rhizosphere sampling points.
SER deviates from optimal stoichiometry by >25% at sites with AES encroachment score > 0.55 — tested via ¹³C/³¹P isotope tracing at 87 interfaces.
∇C navigates hyphae within ±8° of optimal trajectory (p < 0.001) — verified across 2,400 hyphal tip tracking events via time-lapse confocal microscopy.
ABI ratio H′_rhizo / H′_bulk > 1.5 at all intact sites — tested via 16S eDNA sequencing across 312 paired rhizosphere/bulk soil samples.
BFS recovery half-time τ correlates with K_topo at time of disturbance (r > 0.75) — validated across 23 post-fire/logging sites.
AI ensemble MNIS prediction exceeds single-parameter ρ_e prediction by >12% — confirmed via model ablation study across 397 held-out MNU-years.
FUNGI-MYCEL: A Quantitative Framework for Decoding Mycelial Network Intelligence, Bioelectrical Communication, and Sub-Surface Ecological Sovereignty
Nature Microbiology · Springer Nature · Original Research Framework DOI: 10.14293/FUNGI-MYCEL.2026.001 →FUNGI-MYCEL Dataset: 2,648 MNUs from 39 Forest Sites, 5 Biomes — MNIS Scores, Eight-Parameter Measurements, and Validation Records
Zenodo · CERN Data Centre · Open Access Dataset Zenodo Repository →Bioelectrical Spike Train Structure as a Predictor of Mycelial Carbon Sequestration Efficiency across 2,648 MNUs
Nature Ecology & Evolution · Springer Nature Preprint on GitLab →Access the research paper, open-source implementation, and full validation dataset.