The Conscious Universe as an Information Stack: Entanglement, Inference, and Integration at Scale

Douglas C. Youvan

Francisco Di Biase

Bradfield, Winston

This conceptual paper introduces the Unified Informational Substrate (UIS) framework, a speculative but internally coherent model proposing that dark matter functions as a universal, non-local informational field. Within this field, spacetime geometry, quantum coherence, and subjective consciousness emerge as interdependent expressions of underlying informational structure. The UIS model reframes dark matter not as a particle candidate, but as a distributed lattice of integrated information capable of storing, transmitting, and stabilizing patterns across cosmic scales. Consciousness is interpreted as a localized high- (integrated information) pattern, dynamically maintained by the brain as its Physical Substrate of Consciousness (PSC). Quantum measurement is reconceived as an informational integration event, with entanglement and collapse arising from updates in the global informational field. This paper aims to unify key unresolved scientific domains—including the dark matter problem, the quantum measurement problem, and the hard problem of consciousness—under a single informational ontology. The framework also introduces testable predictions linking dark matter density, decoherence behavior, entanglement entropy, and neural coherence signatures. While fully speculative, the UIS framework is designed to be interdisciplinary, falsifiable in principle, and grounded in existing research across cosmology, information theory, integrated information theory (IIT), and emerging quantum gravity models. It represents a contribution toward rethinking the deep informational architecture of reality and motivating new lines of experimental inquiry.

Subhash C. Kak

Rolando P. Hong Enriquez

We propose that neural activity can be rigorously modeled as tensor network states, with cortical hierarchies mapping onto a Multi-scale Entanglement Renormalization Ansatz (MERA) architecture. Within this framework, we identify the effective bond dimension as a measurable neural correlate of consciousness, providing a computationally tractable alternative to integrated information theory. We prove that a tensor-network-based integration measure preserves key properties of established consciousness theories while remaining efficiently computable. The hierarchical organization of the visual cortex—from primary through associative areas—is shown to follow quantitative coarse-graining laws consistent with renormalization group structure. We derive explicit bounds on information capacity and learning complexity, and specify experimental protocols using standard neuroimaging technology. 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. Each claim is assigned an explicit epistemic level, separating proven mathematical theorems from empirically testable hypotheses and speculative interpretations. This approach opens pathways toward objective consciousness diagnostics in disorders of awareness and personalized anesthesia monitoring.

Iheb Laibi