Title: Geometric Modeling of Consciousness through Neural Networks
Zenodo (CERN European Organization for Nuclear Research) September 6, 2026 DOI: 10.5281/zenodo.22515794 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Key points | Proposes that modeling neural networks as geometric structures can represent and analyze consciousness, potentially revealing connections and representations correlated with subjective experience, and outlines a neural network architecture to visualize and quantify this geometry. |
Abstract
This paper explores the potential of geometric modeling to represent and analyze consciousness, proposing a novel neural network architecture designed to explicitly capture the underlying geometric patterns of brain activity. The core claim is that by modeling neural networks as geometric structures, we can gain a deeper understanding of the connections and representations that correlate with subjective experience. We will detail the design principles, mathematical foundations, and potential applications of this approach, focusing on the creation of a model capable of visualizing and quantifying the geometry of neural networks. The research will investigate how this model could potentially shed light on the subjective nature of consciousness.