ArticlePLoS computational biology2020
Tuning network dynamics from criticality to an asynchronous state.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Is criticality a unified setpoint of brain function?Neuron · 2025Pooled it
- Beyond Neural Noise: Critical Dynamics Predict Slower Reaction Times in Adults With and Without ADHD.bioRxiv : the preprint server for biology · 2026Article
- Early life shifts in cortical inhibitory-excitatory balance underlies sensitive periods and skill development.Frontiers in cellular neuroscience · 2026Article
- Opposing Modulation of EEG Aperiodic Component by Ketamine and Thiopental: Implications for the Noninvasive Assessment of Cortical E/I Balance in Humans.Research square · 2025Article
- Self-organized and self-sustained ensemble activity patterns in simulation of mouse primary motor cortex.bioRxiv : the preprint server for biology · 2025Article
- Homeodynamic feedback inhibition control in whole-brain simulations.PLoS computational biology · 2024Article
- Article
- A neurophysiological basis for aperiodic EEG and the background spectral trend.Nature communications · 2024Article
- Article
- Theoretical foundations of studying criticality in the brain.Network neuroscience (Cambridge, Mass.) · 2022Review
- Collapse of complexity of brain and body activity due to excessive inhibition and MeCP2 disruption.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Long range temporal correlations (LRTCs) in MEG-data during emerging psychosis: Relationship to symptoms, medication-status and clinical trajectory.NeuroImage. Clinical · 2021Article
- Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation.Frontiers in computational neuroscience · 2021Review
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2 authors.
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Abstract
According to many experimental observations, neurons in cerebral cortex tend to operate in an asynchronous regime, firing independently of each other. In contrast, many other experimental observations reveal cortical population firing dynamics that are relatively coordinated and occasionally synchronous. These discrepant observations have naturally led to competing hypotheses. A commonly hypothesized explanation of asynchronous firing is that excitatory and inhibitory synaptic inputs are precisely correlated, nearly canceling each other, sometimes referred to as 'balanced' excitation and inhibition. On the other hand, the 'criticality' hypothesis posits an explanation of the more coordinated state that also requires a certain balance of excitatory and inhibitory interactions. Both hypotheses claim the same qualitative mechanism-properly balanced excitation and inhibition. Thus, a natural question arises: how are asynchronous population dynamics and critical dynamics related, how do they differ? Here we propose an answer to this question based on investigation of a simple, network-level computational model. We show that the strength of inhibitory synapses relative to excitatory synapses can be tuned from weak to strong to generate a family of models that spans a continuum from critical dynamics to asynchronous dynamics. Our results demonstrate that the coordinated dynamics of criticality and asynchronous dynamics can be generated by the same neural system if excitatory and inhibitory synapses are tuned appropriately.
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