  Recurrent networks of dynamic elements frequently exhibit emergent collective
oscillations, which can display substantial regularity even when the individual
elements are considerably noisy. How noise-induced dynamics at the local level
coexists with regular oscillations at the global level is still unclear. Here
we show that a combination of stochastic recurrence-based initiation with
deterministic refractoriness in an excitable network can reconcile these two
features, leading to maximum collective coherence for an intermediate noise
level. We report this behavior in the slow oscillation regime exhibited by a
cerebral cortex network under dynamical conditions resembling slow-wave sleep
and anaesthesia. Computational analysis of a biologically realistic network
model reveals that an intermediate level of background noise leads to
quasi-regular dynamics. We verify this prediction experimentally in cortical
slices subject to varying amounts of extracellular potassium, which modulates
neuronal excitability and thus synaptic noise. The model also predicts that
this effectively regular state should exhibit noise-induced memory of the
spatial propagation profile of the collective oscillations, which is also
verified experimentally. Taken together, these results allow us to construe the
enhanced regularity observed experimentally in the brain as an instance of
collective stochastic coherence.
