A Comprehensive Taxonomy of Neural Correlates of Consciousness: Integrated Information, Global Workspace, and State Transitions
Juan Moisés de la Serna de la Serna
Zenodo (CERN European Organization for Nuclear Research) April 16, 2026 DOI: 10.5281/zenodo.19613340 (opens in new tab) via OpenAlex
Summary
AI-generated from the abstractThis work presents the Serna Taxonomy T47, a classification system for neural correlates of consciousness. The taxonomy categorizes different states of consciousness based on brain activity patterns, integrating concepts from neuroscience, computer science, and cognitive science. It proposes a framework to map conscious states to specific neural signatures, drawing on functional brain connectivity studies and research on mind wandering and attention. The taxonomy aims to provide a structured approach for understanding the neural basis of conscious experience, linking theoretical constructs from artificial neural networks and machine learning to empirical observations in neuroscience.
Study at a glance
| Characteristics | Theoretical or philosophical paper Peer reviewed |
|---|---|
| Keywords | Taxonomy biology State computer science Artificial neural network Artificial intelligence Cognitive science |
| Key finding | Proposes a taxonomy for classifying neuroscience concepts. |
Abstract
# Serna Taxonomy T47 Author: Juan Moisés de la Serna ORCID: https://orcid.org/0000-0002-8401-8018 Field: Neuroscience License: CC BY 4.0