Is the brain poised near a critical point?
In plain words
Power-law statistics in brain activity suggest that neural networks operate at a phase transition. Other explanations, such as shared slowly varying inputs, can produce the same statistics without any tuning.
Precise statement
For cortical networks in vivo, determine whether collective activity is at or near a continuous phase transition (avalanche criticality at an absorbing-to-active boundary, or an edge-of-instability point), as opposed to a non-critical state producing power laws by other means (latent common inputs, subsampling, neutral dynamics), and whether the distance to criticality is regulated by homeostasis. An answer states criteria such as finite-size scaling under an independently varied control parameter.
What would settle it
Recordings in which a physiological control parameter is varied and activity shows finite-size scaling and divergent susceptibility at a specific value, excluding latent-variable explanations.
Status in the literature
Unverified note: it names recent work and was partly written without a live literature search; it awaits a check by an editor.
Analyses in 2023 to 2026 report near-critical scaling in mouse and human recordings, while latent-variable models reproduce the same signatures without tuning; no consensus as of 2026.
Problems built on this one
Special cases
- Do neural avalanche exponents obey critical scaling relations? · open
- Universality of neural population coarse-graining exponents · open
Results
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