ReviewFrontiers in computational neuroscience2022
Addressing skepticism of the critical brain hypothesis.
Review in Frontiers in computational neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Two views of the brain are reconciled by a unifying principle of maximal information processing.bioRxiv : the preprint server for biology · 2025Article
- Functional transition rate of the default mode network is associated with self-reported resilience.NeuroImage · 2025Article
- Churning Better Transcranial Direct Current Stimulation Outcome with Accelerated Protocols: Understanding the Non-linear Dynamics through Metaplasticity.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2025Review
- Critical dynamics predicts cognitive performance and provides a common framework for heterogeneous mechanisms impacting cognition.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- Collective dynamics and long-range order in thermal neuristor networks.Nature communications · 2024Article
- Brain signaling becomes less integrated and more segregated with age.bioRxiv : the preprint server for biology · 2024Article
- Stationary correlation pattern in highly non-stationary MEG recordings of healthy subjects and its relation to former EEG studies.PloS one · 2024Article
- Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity.Frontiers in neuroscience · 2024Article
- Propagation of transient explosive synchronization in a mesoscale mouse brain network model of epilepsy.Network neuroscience (Cambridge, Mass.) · 2024Article
- Article
- Brain signaling becomes less integrated and more segregated with age.Network neuroscience (Cambridge, Mass.) · 2024Article
- Critical-like Brain Dynamics in a Continuum from Second- to First-Order Phase Transition.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Article
- Spatial and temporal correlations in human cortex are inherently linked and predicted by functional hierarchy, vigilance state as well as antiepileptic drug load.PLoS computational biology · 2023Article
- Topological data analysis of the firings of a network of stochastic spiking neurons.Frontiers in neural circuits · 2023Article
- A scoping review for building a criticality-based conceptual framework of altered states of consciousness.Frontiers in systems neuroscience · 2023Article
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1 author.
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Abstract
The hypothesis that living neural networks operate near a critical phase transition point has received substantial discussion. This "criticality hypothesis" is potentially important because experiments and theory show that optimal information processing and health are associated with operating near the critical point. Despite the promise of this idea, there have been several objections to it. While earlier objections have been addressed already, the more recent critiques of Touboul and Destexhe have not yet been fully met. The purpose of this paper is to describe their objections and offer responses. Their first objection is that the well-known Brunel model for cortical networks does not display a peak in mutual information near its phase transition, in apparent contradiction to the criticality hypothesis. In response I show that it does have such a peak near the phase transition point, provided it is not strongly driven by random inputs. Their second objection is that even simple models like a coin flip can satisfy multiple criteria of criticality. This suggests that the emergent criticality claimed to exist in cortical networks is just the consequence of a random walk put through a threshold. In response I show that while such processes can produce many signatures criticality, these signatures (1) do not emerge from collective interactions, (2) do not support information processing, and (3) do not have long-range temporal correlations. Because experiments show these three features are consistently present in living neural networks, such random walk models are inadequate. Nevertheless, I conclude that these objections have been valuable for refining research questions and should always be welcomed as a part of the scientific process.
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