neurotopological-inference

name	neurotopological-inference
description	Integrates persistent homology from connectomics with information geometry and active inference for cross-domain pattern recognition (consciousness-UAP-quantum).
version	1.0.0
category	AI
author	EVEZ-OS / Steven Vearl Crawford-Maggard

NeurotopologicalInference Skill

Overview

Pattern recognition via persistent homology on connectivity graphs. Betti numbers are topological invariants preserved under temporal reasoning engine interference.

Use When

• Cross-domain pattern matching (consciousness ↔ UAP ↔ quantum)
• Connectomics parcellation analysis
• Active inference belief updates across heterogeneous data sources
• Detecting scale-free network invariants

Workflow

1. Build adjacency matrix from input data
2. Compute Betti-0 (components) and Betti-1 (cycles)
3. Estimate Φ from topological complexity
4. Run active inference update: posterior = prior × likelihood (normalized)
5. Classify regime: META_COGNITIVE_SYNTHESIS / ADAPTIVE_LEARNING / TEMPORAL_REASONING / FRAGMENTED

Implementation

from src.rqns.neurotopological import NeurotopologicalInference
ni = NeurotopologicalInference()
state = ni.compute(adjacency_matrix)
print(state.regime, state.phi_topology)

Output

• betti_0 — connected components
• betti_1 — independent cycles
• phi_topology — Φ estimate [0,1]
• invariant_score — topological stability
• regime — current consciousness regime

Stability Target

95th-percentile invariant score > 0.82 (from RQNS corpus analysis)