The Topological Signature of Consciousness: A Gromov-Wasserstein Framework for Neural-Phenomenal Alignment
Abstract
We introduce a rigorous methodology using Gromov-Wasserstein (GW) Optimal Trans-port to align the metric space of neural states (N ) with the metric space of functionaldistinctions (Ψ) without assuming a priori coordinate correspondence. Topological DataAnalysis (TDA) provides invariant signatures that persist across coordinate transforma-tions. This framework enables quantitative comparison of structural properties withoutontological claims about the nature of internal representations.
Fields of Study & Subjects
Topological and Geometric Data Analysis
Functional Brain Connectivity Studies
Morphological variations and asymmetry
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 5 January 2026
- Language
- EN
- Document type
- Preprint