Inferring collective dynamical states from widely unobserved systems
Jens Wilting1, Viola Priesemann2,3
1Max-Planck-Institute for Dynamics and Self-Organization, Am Faßberg 17, 37077, Göttingen, Germany.
Nature Communications
|June 15, 2018
Summary
Spatial subsampling often underestimates system instability risk. A new invariant estimator accurately assesses disease infectiousness and reveals complex brain dynamics, offering insights into spatially extended systems.
Area of Science:
- Complex Systems Science
- Epidemiology
- Computational Neuroscience
Background:
- Assessing spatially extended complex systems is challenging due to incomplete data sampling.
- Spatial subsampling can lead to significant underestimation of instability risks in systems with propagating events.
Purpose of the Study:
- To develop a subsampling-invariant estimator for complex systems.
- To apply this estimator to disease spread and neuroscience research.
- To investigate brain dynamics under limited recording conditions.
Main Methods:
- Derivation of a novel subsampling-invariant estimator.
- Application of the estimator to infer disease infectiousness with unreliable case reports.
- Utilizing the estimator to analyze neural recordings from rats, cats, and monkeys.
Main Results:
- The estimator correctly infers disease infectiousness even with subsampled data.
- It accurately assesses the risk of instability in complex systems.
- Analysis of neural data revealed a brain state combining asynchronous-irregular and critical dynamics, allowing long-lasting network reverberation.
Conclusions:
- The subsampling-invariant estimator provides a robust method for analyzing complex systems with limited data.
- It has significant implications for epidemiology, particularly in regions with poor data quality.
- The findings offer new perspectives on neural dynamics and brain function, suggesting a hybrid operational mode.
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