  Spontaneous neuronal activity is a ubiquitous feature of cortex. Its
spatiotemporal organization reflects past input and modulates future network
output. Here we study whether a particular type of spontaneous activity is
generated by a network that is optimized for input processing. Neuronal
avalanches are a type of spontaneous activity observed in superficial cortical
layers in vitro and in vivo with statistical properties expected from a network
in a 'critical state'. Theory predicts that the critical state and, therefore,
neuronal avalanches are optimal for input processing, but until now, this is
untested in experiments. Here, we use cortex slice cultures grown on planar
microelectrode arrays to demonstrate that cortical networks which generate
neuronal avalanches benefit from maximized dynamic range, i.e. the ability to
respond to the greatest range of stimuli. By changing the ratio of excitation
and inhibition in the cultures, we derive a network tuning curve for stimulus
processing as a function of distance from the critical state in agreement with
predictions from our simulations. Our findings suggest that in the cortex, (1)
balanced excitation and inhibition establishes the critical state, which
maximizes the range of inputs that can be processed and (2) spontaneous
activity and input processing are unified in the context of critical phenomena.
