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Universal Critical Dynamics in High Resolution Neuronal Avalanche Data

2012· article· en· 514 citations· W2061002342 on OpenAlex· 10.1103/physrevlett.108.208102

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.

No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Opus teacher head0.052
GPT teacher head0.315
Teacher spread
0.263 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

The tasks of neural computation are remarkably diverse. To function optimally, neuronal networks have been hypothesized to operate near a nonequilibrium critical point. However, experimental evidence for critical dynamics has been inconclusive. Here, we show that the dynamics of cultured cortical networks are critical. We analyze neuronal network data collected at the individual neuron level using the framework of nonequilibrium phase transitions. Among the most striking predictions confirmed is that the mean temporal profiles of avalanches of widely varying durations are quantitatively described by a single universal scaling function. We also show that the data have three additional features predicted by critical phenomena: approximate power law distributions of avalanche sizes and durations, samples in subcritical and supercritical phases, and scaling laws between anomalous exponents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

The record

Venue
Physical Review Letters
Topic
Neural dynamics and brain function
Field
Neuroscience
Canadian institutions
Funders
Division of Materials ResearchJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
Keywords
Statistical physicsCritical point (mathematics)ScalingCritical exponentNon-equilibrium thermodynamicsPower lawPhysicsCritical phenomenaPhase transitionFunction (biology)Scaling lawComputationDynamics (music)Computer scienceQuantum mechanicsAlgorithmMathematics
Has abstract in OpenAlex
yes