Tensor Network Neuroscience: A Rigorous Mathematical Framework
Zenodo (CERN European Organization for Nuclear Research) May 7, 2026 Rolando Pablo Hong Enriquez
Neural activity can be modeled as tensor network states, with cortical hierarchies mapping onto a Multi-scale Entanglement Renormalization Ansatz (MERA) architecture. The effective bond dimension is identified as a measurable neural correlate of consciousness, offering a computationally tractable alternative to integrated information theory. A tensor-network-based integration measure preserves key properties of established consciousness theories while remaining efficiently computable. The hierarchical organization of the visual cortex follows quantitative coarse-graining laws consistent with renormalization group structure. The framework yields falsifiable predictions: bond dimension values across cortical regions, exponential decay of bond dimension under anesthesia, and a universal consciousness threshold at loss of consciousness.