{
  "abstract": "Understanding complex systems requires characterizing how they perform distributed computation and integrate information. Information theory offers several quantities for describing complex information structures, where collective patterns of coordination emerge from higher-order (i.e., beyond-pairwise) interdependencies. However, applying these approaches to large systems is severely hindered by their poor scalability, and few measures are specifically designed for multivariate time series data. Here, we introduce M -information, a novel measure that quantifies the higher-order information integration in complex dynamical systems. We show that M -information can be calculated via a convex optimization problem and derive a robust, efficient algorithm that scales gracefully with system size. M -information is resilient to noise, indexes critical behavior in artificial neuronal populations, and tracks consciousness and task performance in macaque and mouse neuroimaging data. Finally, M -information can be incorporated into existing information decomposition frameworks to reveal a comprehensive taxonomy of information dynamics, helping unravel collective computation in large complex systems.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computing, Imperial College London, London, UK; Corresponding author",
    "Department of Experimental Psychology, University College London, London, UK",
    "Center for Complexity Science, Department of Mathematics, Imperial College London, London, UK",
    "Center for Complexity Science, Department of Mathematics, Imperial College London, London, UK; Sussex AI and Sussex Centre for Consciousness Science, University of Sussex, Brighton, UK",
    "Department of Computing, Imperial College London, London, UK; Department of Experimental Psychology, University College London, London, UK"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Alberto Liardi"
    },
    {
      "affs": [
        1
      ],
      "name": "George Blackburne"
    },
    {
      "affs": [
        2
      ],
      "name": "Hardik Rajpal"
    },
    {
      "affs": [
        3
      ],
      "name": "Fernando E. Rosas"
    },
    {
      "affs": [
        4
      ],
      "name": "Pedro A.M. Mediano"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/275wr1brvrdbz.bib",
  "cited_by": 0,
  "doi": "10.1016/j.xcrp.2026.103550",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S266638642600456X",
  "id": "275wr1brvrdbz",
  "issns": [
    "2666-3864"
  ],
  "landing_url": "https://doi.org/10.1016/j.xcrp.2026.103550",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QC1-999"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/275wr1brvrdbz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:43:37Z",
      "native_id": "e61209c822784947badb7a3b80996b23",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.xcrp.2026.103550"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "lcc:QC1-999",
      "subfield": "general"
    }
  ],
  "title": "A scalable estimator of higher-order information in complex dynamical systems",
  "title_text": "A scalable estimator of higher-order information in complex dynamical systems",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:37Z",
  "url": "https://research.draconitic.com/paper/275wr1brvrdbz",
  "venue": "Cell Reports Physical Science",
  "versions": []
}