ArticleCerebral cortex (New York, N.Y. : 1991)2018
Random Recurrent Networks Near Criticality Capture the Broadband Power Distribution of Human ECoG Dynamics.
Article in Cerebral cortex (New York, N.Y. : 1991), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Is criticality a unified setpoint of brain function?Neuron · 2025Pooled it
- Field potential 1/Journal of neurophysiology · 2019Trial
- Schizophrenia: temporal imprecision in phase, time, and frequency domains.Schizophrenia (Heidelberg, Germany) · 2026Article
- Neural timescales from a computational perspective.Nature neuroscience · 2026Review
- Aperiodicity in Mouse CA1 and DG Power Spectra.eNeuro · 2026Article
- Frequency-specific microstate correlates of ciprofol-induced alterations of consciousness.Frontiers in neuroscience · 2026Article
- A hierarchy of time constants and reliable signal propagation in the marmoset cerebral cortex.Nature communications · 2025Article
- Adaptive proximity to criticality underlies amplification of ultra-slow fluctuations during free recall.PLoS computational biology · 2025Article
- Large-scale maps of altered cortical dynamics in early-stage psychosis are related to GABAergic and glutamatergic neurotransmission.Science advances · 2025Article
- Relationships Between Spontaneous Alpha Oscillation and Brain Response Amid the Complexity of Brain Adaptation and Spectral Signal Composition.Psychophysiology · 2025Article
- Contributions of action potentials to scalp EEG: Theory and biophysical simulations.PLoS computational biology · 2025Article
- Task-related changes in aperiodic activity are related to visual working memory capacity independent of event-related potentials and alpha oscillations.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Slow ramping emerges from spontaneous fluctuations in spiking neural networks.Nature communications · 2024Article
- A General, Noise-Driven Mechanism for the 1/f-Like Behavior of Neural Field Spectra.Neural computation · 2024Article
- A neurophysiological basis for aperiodic EEG and the background spectral trend.Nature communications · 2024Article
- Comparative analysis of multifaceted neural effects associated with varying endogenous cognitive load.Communications biology · 2023Article
- Slope of the power spectral density flattens at low frequencies (<150 Hz) with healthy aging but also steepens at higher frequency (>200 Hz) in human electroencephalogram.Cerebral cortex communications · 2023Article
- Robust estimation of 1/f activity improves oscillatory burst detection.The European journal of neuroscience · 2022Article
- Intrinsic dynamics and topography of sensory input systems.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Alpha blocking and 1/fβ spectral scaling in resting EEG can be accounted for by a sum of damped alpha band oscillatory processes.PLoS computational biology · 2022Article
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3 authors.
Funding
Abstract
Brain electric field potentials are dominated by an arrhythmic broadband signal, but the underlying mechanism is poorly understood. Here we propose that broadband power spectra characterize recurrent neural networks of nodes (neurons or clusters of neurons), endowed with an effective balance between excitation and inhibition tuned to keep the network on the edge of dynamical instability. These networks show a fast mode reflecting local dynamics and a slow mode emerging from distributed recurrent connections. Together, the 2 modes produce power spectra similar to those observed in human intracranial EEG (i.e., electrocorticography, ECoG) recordings. Moreover, such networks convert spatial input correlations across nodes into temporal autocorrelation of network activity. Consequently, increased independence between nodes reduces low-frequency power, which may explain changes observed during behavioral tasks. Lastly, varying network coupling causes activity changes that resemble those observed in human ECoG across different arousal states. The model links macroscopic features of empirical ECoG power to a parsimonious underlying network structure, and suggests mechanisms for changes observed across behavioral and arousal states. This work provides a computational framework to generate and test hypotheses about cellular and network mechanisms underlying whole brain electrical dynamics, their variations across states, and potential alterations in brain diseases.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.