ArticleCerebral cortex (New York, N.Y. : 1991)2019
Between Perfectly Critical and Fully Irregular: A Reverberating Model Captures and Predicts Cortical Spike Propagation.
Article in Cerebral cortex (New York, N.Y. : 1991), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Is criticality a unified setpoint of brain function?Neuron · 2025Pooled it
- Multiscale characterization and classification of Alzheimer's disease via integration of brain fingerprint radiomics and graph‑theoretical network metrics.Neuroradiology · 2026Article
- Hippocampal criticality tracks cognitive demand and shifts with memory impairment.Communications biology · 2026Article
- Single-Neuron Critical Intermittency in a Stochastic Hodgkin-Huxley Model.The European journal of neuroscience · 2025Article
- Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity.Communications biology · 2024Article
- Signatures of hierarchical temporal processing in the mouse visual system.PLoS computational biology · 2024Article
- A neurophysiological basis for aperiodic EEG and the background spectral trend.Nature communications · 2024Article
- A thalamocortical substrate for integrated information via critical synchronous bursting.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Criticality of resting-state EEG predicts perturbational complexity and level of consciousness during anesthesia.bioRxiv : the preprint server for biology · 2023Article
- Propagation of activity through the cortical hierarchy and perception are determined by neural variability.Nature neuroscience · 2023Article
- Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data.Nature communications · 2023Article
- Disordered network structure and function in dystonia: pathological connectivity vs. adaptive responses.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Neurodegenerative disease of the brain: a survey of interdisciplinary approaches.Journal of the Royal Society, Interface · 2023Review
- Sampling effects and measurement overlap can bias the inference of neuronal avalanches.PLoS computational biology · 2022Article
- Replay, the default mode network and the cascaded memory systems model.Nature reviews. Neuroscience · 2022Review
- Quasicriticality explains variability of human neural dynamics across life span.Frontiers in computational neuroscience · 2022Article
- Toward a Unified Analysis of the Brain Criticality Hypothesis: Reviewing Several Available Tools.Frontiers in neural circuits · 2022Review
- Addressing skepticism of the critical brain hypothesis.Frontiers in computational neuroscience · 2022Review
- Theoretical foundations of studying criticality in the brain.Network neuroscience (Cambridge, Mass.) · 2022Review
- Multiscale criticality measures as general-purpose gauges of proper brain function.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowledge about the collective dynamics of cortical spiking is very informative about the underlying coding principles. However, even most basic properties are not known with certainty, because their assessment is hampered by spatial subsampling, i.e., the limitation that only a tiny fraction of all neurons can be recorded simultaneously with millisecond precision. Building on a novel, subsampling-invariant estimator, we fit and carefully validate a minimal model for cortical spike propagation. The model interpolates between two prominent states: asynchronous and critical. We find neither of them in cortical spike recordings across various species, but instead identify a narrow "reverberating" regime. This approach enables us to predict yet unknown properties from very short recordings and for every circuit individually, including responses to minimal perturbations, intrinsic network timescales, and the strength of external input compared to recurrent activation "thereby informing about the underlying coding principles for each circuit, area, state and task.
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