It has been shown experimentally that spontaneous cortical activity in the absence of sensory inputs modulates stimulus-evoked activity and is correlated with behavior. In the visual cortex, there is a close relationship between ongoing spontaneous activity and the spontaneous firing of a single neuron. There are dynamic switchings amongst these spontaneous cortical states, which may span several hypercolumns spatially and are closely associated to orientation maps. To study theoretically these spatially coherent patterns of spontaneous activity, which emerge in a fluctuation-dominated neuronal network with anisotropic long-range cortical couplings in addition to isotropic short-range interactions, we have developed a coarse-grained representation of neuronal network dynamics in terms of (1+1)-D kinetic equations, which are derived via a novel moment closure, directly from the original large-scale integrate-and-fire I&F network. This powerful kinetic theory captures the full dynamic range of neuronal networks --- from the mean-driven limit (a limit such as the number of neurons →∞, in which the fluctuations vanish) to the fluctuation-dominated limit (such as in small N networks or sparsely connected networks). Comparison with full numerical simulations of the original I&F network establishes that our kinetic theory and embedded network approach are dynamically very accurate and numerically extremely efficient.