  A rigorous understanding of brain dynamics and function requires a conceptual
bridge between multiple levels of organization, including neural spiking and
network-level population activity. Mounting evidence suggests that neural
networks of cerebral cortex operate at criticality. How operating near this
network state impacts the variability of neuronal spiking is largely unknown.
Here we show in a computational model that two prevalent features of cortical
single-neuron activity, irregular spiking and the decline of response
variability at stimulus onset, are both emergent properties of a recurrent
network operating near criticality. Importantly, our work reveals that the
relation between the irregularity of spiking and the number of input
connections to a neuron, i.e., the in-degree, is maximized at criticality. Our
findings establish criticality as a unifying principle for the variability of
single-neuron spiking and the collective behavior of recurrent circuits in
cerebral cortex.
