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Inferring collective dynamical states from widely unobserved systems

Author

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  • Jens Wilting

    (Max-Planck-Institute for Dynamics and Self-Organization)

  • Viola Priesemann

    (Max-Planck-Institute for Dynamics and Self-Organization
    Bernstein-Center for Computational Neuroscience)

Abstract

When assessing spatially extended complex systems, one can rarely sample the states of all components. We show that this spatial subsampling typically leads to severe underestimation of the risk of instability in systems with propagating events. We derive a subsampling-invariant estimator, and demonstrate that it correctly infers the infectiousness of various diseases under subsampling, making it particularly useful in countries with unreliable case reports. In neuroscience, recordings are strongly limited by subsampling. Here, the subsampling-invariant estimator allows to revisit two prominent hypotheses about the brain’s collective spiking dynamics: asynchronous-irregular or critical. We identify consistently for rat, cat, and monkey a state that combines features of both and allows input to reverberate in the network for hundreds of milliseconds. Overall, owing to its ready applicability, the novel estimator paves the way to novel insight for the study of spatially extended dynamical systems.

Suggested Citation

  • Jens Wilting & Viola Priesemann, 2018. "Inferring collective dynamical states from widely unobserved systems," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04725-4
    DOI: 10.1038/s41467-018-04725-4
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    Citations

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    Cited by:

    1. Yang Yiling & Katharine Shapcott & Alina Peter & Johanna Klon-Lipok & Huang Xuhui & Andreea Lazar & Wolf Singer, 2023. "Robust encoding of natural stimuli by neuronal response sequences in monkey visual cortex," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    2. Contreras, Sebastian & Oróstica, Karen Y. & Daza-Sanchez, Anamaria & Wagner, Joel & Dönges, Philipp & Medina-Ortiz, David & Jara, Matias & Verdugo, Ricardo & Conca, Carlos & Priesemann, Viola & Oliver, 2023. "Model-based assessment of sampling protocols for infectious disease genomic surveillance," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    3. Benoit Duchet & Filippo Ghezzi & Gihan Weerasinghe & Gerd Tinkhauser & Andrea A Kühn & Peter Brown & Christian Bick & Rafal Bogacz, 2021. "Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson’s disease," PLOS Computational Biology, Public Library of Science, vol. 17(7), pages 1-42, July.
    4. Lucas Rudelt & Daniel González Marx & Michael Wibral & Viola Priesemann, 2021. "Embedding optimization reveals long-lasting history dependence in neural spiking activity," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-51, June.
    5. Annika Hagemann & Jens Wilting & Bita Samimizad & Florian Mormann & Viola Priesemann, 2021. "Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-18, March.
    6. F P Spitzner & J Dehning & J Wilting & A Hagemann & J P. Neto & J Zierenberg & V Priesemann, 2021. "MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-21, April.
    7. Forough Habibollahi & Brett J. Kagan & Anthony N. Burkitt & Chris French, 2023. "Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    8. Jiawei Xu & Yincai Tang, 2021. "Bayesian Framework for Multi-Wave COVID-19 Epidemic Analysis Using Empirical Vaccination Data," Mathematics, MDPI, vol. 10(1), pages 1-22, December.

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