Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 35 min ago
Peer-reviewedCC BYCell Reports Physical Science

A scalable estimator of higher-order information in complex dynamical systems

  • Alberto Liardi1
  • George Blackburne2
  • Hardik Rajpal3
  • Fernando E. Rosas4
  • Pedro A.M. Mediano5
5 affiliations
  1. 1Department of Computing, Imperial College London, London, UK; Corresponding author
  2. 2Department of Experimental Psychology, University College London, London, UK
  3. 3Center for Complexity Science, Department of Mathematics, Imperial College London, London, UK
  4. 4Center for Complexity Science, Department of Mathematics, Imperial College London, London, UK; Sussex AI and Sussex Centre for Consciousness Science, University of Sussex, Brighton, UK
  5. 5Department of Computing, Imperial College London, London, UK; Department of Experimental Psychology, University College London, London, UK
Read full textDOICite

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.

Related

  1. Nonlinear Dynamical Systems as Scalable and Efficient Physical Neural Networks
    Tyler King, Magnus Saebo, Jeremie Laydevant +4
    arXivPreprintCC BY 4.0Full textCondensed matterarXiv
  2. Efficiently and Reliably Measuring Information Processing Capacity in Dynamical Systems via Kernels
    Alessio Benavoli, Felix Binder, Cangxiong Chen
    arXivPreprintarXiv licenceFull textDynamical systemsQuantum physics & quantum informationarXiv
  3. BEHAVE: Real-Time Modeling of Human Systems as Observable Complex Dynamical Systems and Operational Objects for Physical AI
    Helene Malyutina
    arXivPreprintarXiv licenceFull textStatistical & nonlinear physicsarXiv
  4. Scalable construction of force-constant basis sets for large-scale anharmonic lattice-dynamical calculations
    Atsuto Seko, Atsushi Togo
    arXivPreprintarXiv licenceFull textComputational physicsCondensed matterarXiv
  5. Scalable Neural Wave Functions via Information Mixing and Hierarchical Network Architectures
    Faith Oyedemi, Stefanie Czischek
    arXivPreprintarXiv licenceFull textCondensed matterQuantum physics & quantum informationarXiv

Cite

BibTeXRIS
BibTeX
@article{liardi2026scalable,
  title = {{A scalable estimator of higher-order information in complex dynamical systems}},
  author = {Alberto Liardi and George Blackburne and Hardik Rajpal and Fernando E. Rosas and Pedro A.M. Mediano},
  journal = {Cell Reports Physical Science},
  year = {2026},
  month = oct,
  doi = {10.1016/j.xcrp.2026.103550},
  url = {https://doi.org/10.1016/j.xcrp.2026.103550},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - A scalable estimator of higher-order information in complex dynamical systems
AU  - Alberto Liardi
AU  - George Blackburne
AU  - Hardik Rajpal
AU  - Fernando E. Rosas
AU  - Pedro A.M. Mediano
PY  - 2026
DA  - 2026/10/23
JO  - Cell Reports Physical Science
DO  - 10.1016/j.xcrp.2026.103550
UR  - https://doi.org/10.1016/j.xcrp.2026.103550
AB  - 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.
SN  - 2666-3864
ER  -