  The brain keeps its overall dynamics in a corridor of intermediate activity
and it has been a long standing question what possible mechanism could achieve
this task. Mechanisms from the field of statistical physics have long been
suggesting that this homeostasis of brain activity could occur even without a
central regulator, via self-organization on the level of neurons and their
interactions, alone. Such physical mechanisms from the class of self-organized
criticality exhibit characteristic dynamical signatures, similar to seismic
activity related to earthquakes. Measurements of cortex rest activity showed
first signs of dynamical signatures potentially pointing to self-organized
critical dynamics in the brain. Indeed, recent more accurate measurements
allowed for a detailed comparison with scaling theory of non-equilibrium
critical phenomena, proving the existence of criticality in cortex dynamics. We
here compare this new evaluation of cortex activity data to the predictions of
the earliest physics spin model of self-organized critical neural networks. We
find that the model matches with the recent experimental data and its
interpretation in terms of dynamical signatures for criticality in the brain.
The combination of signatures for criticality, power law distributions of
avalanche sizes and durations, as well as a specific scaling relationship
between anomalous exponents, defines a universality class characteristic of the
particular critical phenomenon observed in the neural experiments. The spin
model is a candidate for a minimal model of a self-organized critical adaptive
network for the universality class of neural criticality. As a prototype model,
it provides the background for models that include more biological details, yet
share the same universality class characteristic of the homeostasis of activity
in the brain.
