Thilo Womelsdorf et al. investigate the modulation of neuronal interactions through synchronization of rhythmic activities within neuronal groups. They found that the mutual influence between neuronal groups depends on the phase relation between their rhythmic activities, with phase relations supporting interactions preceding those interactions by a few milliseconds. This effect was specific in time, frequency, and space, suggesting that the pattern of synchronization flexibly determines the pattern of neuronal interactions. The study analyzed multiunit activity (MUA) and local field potentials (LFPs) from four data sets, including awake cat and monkey areas. The results showed that phase coherence was not perfect but assumed average peak values, and the mutual influence between neuronal groups was significantly stronger when the phase relation was close to its mean. The effect was consistent across different data sets and generalized to long-range interactions. The authors propose that the pattern of synchronization (its precision, phase, or both) weights the anatomical-connection infrastructure with a gain pattern, resulting in an effective interaction pattern. This mechanism could dynamically modify the effective interaction pattern, act connection-wise, and contribute to spike-time-dependent plasticity and long-term traces. Synchronization might emerge self-organized between "matching" neuronal groups, potentially contributing to the selective routing of sensory information to behavioral control.Thilo Womelsdorf et al. investigate the modulation of neuronal interactions through synchronization of rhythmic activities within neuronal groups. They found that the mutual influence between neuronal groups depends on the phase relation between their rhythmic activities, with phase relations supporting interactions preceding those interactions by a few milliseconds. This effect was specific in time, frequency, and space, suggesting that the pattern of synchronization flexibly determines the pattern of neuronal interactions. The study analyzed multiunit activity (MUA) and local field potentials (LFPs) from four data sets, including awake cat and monkey areas. The results showed that phase coherence was not perfect but assumed average peak values, and the mutual influence between neuronal groups was significantly stronger when the phase relation was close to its mean. The effect was consistent across different data sets and generalized to long-range interactions. The authors propose that the pattern of synchronization (its precision, phase, or both) weights the anatomical-connection infrastructure with a gain pattern, resulting in an effective interaction pattern. This mechanism could dynamically modify the effective interaction pattern, act connection-wise, and contribute to spike-time-dependent plasticity and long-term traces. Synchronization might emerge self-organized between "matching" neuronal groups, potentially contributing to the selective routing of sensory information to behavioral control.