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The brain keeps its overall dynamics in a corridor of intermediate activityand it has been a long standing question what possible mechanism could achievethis task. Mechanisms from the field of statistical physics have long beensuggesting that this homeostasis of brain activity could occur even without acentral regulator, via self-organization on the level of neurons and theirinteractions, alone. Such physical mechanisms from the class of self-organizedcriticality exhibit characteristic dynamical signatures, similar to seismicactivity related to earthquakes. Measurements of cortex rest activity showedfirst signs of dynamical signatures potentially pointing to self-organizedcritical dynamics in the brain. Indeed, recent more accurate measurementsallowed for a detailed comparison with scaling theory of non-equilibriumcritical phenomena, proving the existence of criticality in cortex dynamics. Wehere compare this new evaluation of cortex activity data to the predictions ofthe earliest physics spin model of self-organized critical neural networks. Wefind that the model matches with the recent experimental data and itsinterpretation in terms of dynamical signatures for criticality in the brain.The combination of signatures for criticality, power law distributions ofavalanche sizes and durations, as well as a specific scaling relationshipbetween anomalous exponents, defines a universality class characteristic of theparticular critical phenomenon observed in the neural experiments. The spinmodel is a candidate for a minimal model of a self-organized critical adaptivenetwork for the universality class of neural criticality. As a prototype model,it provides the background for models that include more biological details, yetshare the same universality class characteristic of the homeostasis of activityin the brain.

Avalanches in self-organized critical neural networks: A minimal model for the neural SOC universality class