Brain function depends on segregation and integration of information processing in brain networks often separated by long-range anatomic connections. Neuronal oscillations orchestrate such distributed processing through transient amplitude and phase coupling, yet surprisingly, little is known about local network properties facilitating these functional connections. Here, we test whether criticality, a dynamical state characterized by scale-free oscillations, optimizes the capacity of neuronal networks to couple through amplitude or phase, and transfer information. We coupledin siliconetworks which exhibit oscillations in the α band (8–16 Hz), and varied excitatory and inhibitory connectivity. We found that phase coupling of oscillations emerges at criticality, and that amplitude coupling, as well as information transfer, are maximal when networks are critical. Importantly, regulating criticality through modulation of synaptic gain showed that critical dynamics, as opposed to a static ratio of excitatory and inhibitory connections, optimize network coupling and information transfer. Our data support the idea that criticality is important for local and global information processing and may help explain why brain disorders characterized by local alterations in criticality also exhibit impaired long-range synchrony, even before degeneration of axonal connections.SIGNIFICANCE STATEMENTTo perform adaptively in a changing environment, our brains dynamically coordinate activity across distant areas. Empirical evidence suggests that long-range amplitude and phase coupling of oscillations are systems-level mechanisms enabling transient formation of spatially distributed functional networks on the backbone of a relatively static structural connectome. However, surprisingly little is known about the local network properties that optimize coupling and information transfer. Here, we show that criticality, a dynamical state characterized by scale-free oscillations and a hallmark of neuronal network activity, optimizes the capacity of neuronal networks to couple through amplitude or phase and transfer information.