ArticleProceedings of the National Academy of Sciences of the United States of America2025
Toward a unified taxonomy of information dynamics via Integrated Information Decomposition.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- The entropic brain today.Brain : a journal of neurology · 2026Review
- Article
- Structured Fluctuations and the Information Dynamics of Self-Maintenance in Growing Neural Cellular Automata.Entropy (Basel, Switzerland) · 2026Article
- Charting higher-order models of brain function beyond pairwise interactions.Nature communications · 2026Article
- Synergistic and redundant information dynamics exhibit dissociable alterations across schizophrenia and neurodevelopmental conditions.Brain informatics · 2026Article
- Community First Theory: How Collective Organization Generates Individual Diversity.Entropy (Basel, Switzerland) · 2026Article
- Structural signatures of synergy and redundancy in human brain function.bioRxiv : the preprint server for biology · 2026Article
- Competitive interactions shape mammalian brain network dynamics and computation.Nature neuroscience · 2026Article
- Convergent transcriptomic and connectomic controllers of information integration and its anaesthetic breakdown across mammalian brains.Nature human behaviour · 2026Article
- Spatiotemporal complexity in the psychotic brain.Molecular psychiatry · 2026Article
- Evolving reservoir computers reveal bidirectional coupling between predictive power and emergent dynamics.Patterns (New York, N.Y.) · 2026Article
- Synergistic and redundant information dynamics are modulated by Alzheimer's disease and cognitive impairment.bioRxiv : the preprint server for biology · 2026Article
- Towards an informational account of interpersonal coordination.Nature reviews. Neuroscience · 2026Review
- Dynamic brain synergy uncovers functional neural coordination in Parkinson's disease under dopaminergic modulation.Journal of neuroengineering and rehabilitation · 2026Article
- From chemistry to cognition: the adaptive origins of consciousness under constraint.Frontiers in neuroscience · 2026Article
- Synergy mediates long-range correlations in the visual cortex near criticality.Frontiers in computational neuroscience · 2026Article
- The motion sensitivity and predictive utility of different estimates of interregional functional coupling in resting-state functional MRI.Network neuroscience (Cambridge, Mass.) · 2026Article
- Emergent multiscale organisation of neural dynamics fragments in anaesthesia.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Synergy mediates Long-Range Correlations in the Visual Cortex Near Criticality.bioRxiv : the preprint server for biology · 2025Article
- Understanding the high-order network plasticity mechanisms of ultrasound neuromodulation.PLoS computational biology · 2025Article
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7 authors.
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Abstract
Our ability to understand and control complex systems of many interacting parts remains limited. A key challenge is that we still do not know how best to describe-and quantify-the many-to-many dynamical interactions that characterize their complexity. To address this limitation, we introduce the mathematical framework of Integrated Information Decomposition, or [Formula: see text]ID. [Formula: see text]ID provides a comprehensive framework to disentangle and characterize the information dynamics of complex multivariate systems. On the theoretical side, [Formula: see text]ID reveals the existence of previously unreported modes of collective information flow, providing tools to express well-known measures of information transfer, information storage, and dynamical complexity as aggregates of these modes, thereby overcoming some of their known theoretical shortcomings. On the empirical side, we validate our theoretical results with computational models and examples from over 1,000 biological, social, physical, and synthetic dynamical systems. Altogether, [Formula: see text]ID improves our understanding of the behavior of widely used measures for characterizing complex systems across disciplines and leads to new more refined analyses of dynamical complexity.
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