Editorial: Complex network dynamics in consciousness.
Front Comput Neurosci November 1, 2023 Francisco J. Esteban, Antonio Ibáñez-Molina, Sergio Iglesias-Parro et al. 1 citation
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Fernando Soler-Toscano
3 papers in the library · 1 citation · publishing 2018-2026
Papers
Synchronization, Information, and Brain Dynamics in Consciousness Research
Applied Sciences January 27, 2026 Francisco J. Esteban, Eva Vargas, José A. Langa et al.
Consciousness can be understood through a dynamical view of brain processing that combines three complementary frameworks: continuous formulations of Integrated Information Theory, attractor-landscape modeling of brain-state transitions, and perturbational complexity metrics from TMS-EEG. Consciousness emerges near criticality, where metastable attractors allow flexible transitions between partially synchronized states. Perturbational-complexity indices quantify the brain's capacity for integration and differentiation even without behavioral responsiveness. Across anesthesia, disorders of consciousness, epilepsy, and neurodegeneration, TMS-EEG biomarkers show reduced complexity and altered synchronization consistent with structural and functional disconnection. Integrating multimodal data supports individualized modeling of consciousness-related dynamics.
Informational structures: A dynamical system approach for integrated information.
PLoS Computational Biology September 1, 2018 Francisco J. Esteban, Javier A. Galadí, José A. Langa et al.
Integrated Information Theory (IIT) offers a mathematical framework for consciousness, identifying conscious experience with a conceptual structure that is composed of parts, informative, integrated, and maximally irreducible. This paper extends IIT by introducing a space-time continuous version of integrated information. Using graph and dynamical systems approaches, it defines an Informational Structure for a mechanism in a given state, associated with the system's global attractor over time. This structure determines all past and future behavior, enriches phase space points with cause-effect power via an Informational Field, and allows a measure of integrated information through invariants and transition probability matrices.