Universal critical dynamics in high resolution neuronal avalanche data

Nir Friedman, Shinya Ito, Braden A.W. Brinkman, Masanori Shimono, R. E.Lee Deville, Karin A. Dahmen, John M. Beggs, Thomas C. Butler

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number208102
JournalPhysical review letters
Issue number20
StatePublished - May 16 2012

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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