Complex systems, when poised at the transition between order and disorder, exhibit scale-free, power law dynamics.  These critical systems are highly adaptive and flexibly process and store information, which for decades prompted the conjecture that the brain might operate at criticality.  Our discovery of neuronal avalanches in superficial layers of cortex in 2001 provided solid experimental evidence that indeed the brain might be critical. The spatio-temporal, synchronized activity patterns of avalanches form a scale-free organization that spontaneously emerges in vitro in slice cultures and acute slices and in vivo in the anesthetized rat. We recently demonstrated that ongoing activity in awake monkeys is composed of neuronal avalanches.  This introduces criticality as a precise, quantitative framework of the awake state that allows cortex to expand during development and evolution without fundamental changes in its architecture.   

Avalanches are established at the time of superficial cortex layer differentiation, require balanced fast excitation and inhibition, and are regulated via an inverted-U profile of NMDA/dopamine-D1 interaction, well known from cognitive task paradigms, e.g. working memory.  Their internal organization forms a small-world topology that combines local diversity with efficient global communication. Neuronal synchronization in the form of avalanches naturally incorporates gamma-oscillations and cascades, e.g., synfire chains. The size and timing of a single avalanche is governed by two fundamental power laws, which are equivalent to those found for other critical systems, e.g. the Gutenberg-Richter law for earthquake sizes and the Omori-law, which describes the occurrences of aftershocks following a main earthquake.

Overall, our results suggest that neuronal avalanches indicate a critical network dynamic at which the cortex gains universal properties found at criticality. These properties constitute a novel framework that allows for a precise quantification of cortex function such as the absolute discrimination of pathological from non-pathological synchronization, and the identification of maximal dynamic range for input-output processing.