Inferring collective dynamical states from widely unobserved systems
arXiv:1608.07035 · doi:10.1038/s41467-018-04725-4
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.
7 pages + 12 pages supplementary information + 7 supplementary figures. Title changed to match journal reference
References in corpus (5)
Cited by in corpus (30)
- Inferring change points in the COVID-19 spreading reveals the effectiveness of interventions
- Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data
- Distributions of covariances as a window into the operational regime of neuronal networks
- Two types of criticality in the brain
- Control of criticality and computation in spiking neuromorphic networks with plasticity
- Evidence for quasicritical brain dynamics
- Between perfectly critical and fully irregular: a reverberating model captures and predicts cortical spike propagation
- Self-organization toward criticality by synaptic plasticity
- Adaptive, locally-linear models of complex dynamics
- Models of communication and control for brain networks: distinctions, convergence, and future outlook
- Homeostatic plasticity and external input shape neural network dynamics
- Hybrid-type synchronization transitions: where marginal coherence, scale-free avalanches, and bistability live together
- Antiepileptic drugs induce subcritical dynamics in human cortical networks
- Low case numbers enable long-term stable pandemic control without lockdowns
- Dynamic Adaptive Computation: Tuning network states to task requirements
- Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex
- Tackling the subsampling problem to infer collective properties from limited data
- Emergence of localized persistent weakly-evanescent cortical brain wave loops
- Critical neuronal models with relaxed timescales separation
- Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence
- Tailored ensembles of neural networks optimize sensitivity to stimulus statistics
- MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity
- Tracking the distance to criticality in systems with unknown noise
- Self-consistent formulations for stochastic nonlinear neuronal dynamics
- A Microscopic Theory of Intrinsic Timescales in Spiking Neural Networks
- Sampling effects and measurement overlap can bias the inference of neuronal avalanches
- Model-based assessment of sampling protocols for infectious disease genomic surveillance
- Characterizing spreading dynamics of subsampled systems with non-stationary external input
- From reductionism to realism: Holistic mathematical modelling for complex biological systems
- Sandpile cascades on oscillator networks: the BTW model meets Kuramoto