Title: Geometric Modeling of Consciousness: Hierarchical Representations and Predictive Chaos
Zenodo (CERN European Organization for Nuclear Research) September 7, 2026 DOI: 10.5281/zenodo.22593597 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key points | Proposes that a dynamic graph model incorporating chaotic systems can represent consciousness and generate patterns suggestive of emergent cognitive phenomena, offering a novel perspective on information processing. |
Abstract
This paper explores the potential of geometric modeling to represent consciousness, moving beyond correlational analysis to propose a hierarchical framework based on predictive chaos. The core claim is to develop a dynamic graph model of the brain, where interconnected regions evolve through a chaotic system, and the network's structure reflects internal states. This model aims to offer a novel perspective on information processing and potentially contribute to a more profound understanding of consciousness. We investigate the application of a "Dynamical Graph" approach, incorporating a chaotic system within the network's connections, and demonstrate its ability to generate patterns suggestive of emergent cognitive phenomena. The paper discusses the challenges and opportunities associated with this approach, highlighting the importance of exploring the interplay between geometry, chaos, and the subjective experience of consciousness.