Abstract
Neuroscience research spans multiple spatiotemporal scales, from subsecond dynamics of individual neurons to the slow coordination of billions of neurons during resting state and sleep. Here it is shown that a single functional principle-temporal fluctuations in oscillation peak frequency ("frequency sliding")-can be used as a common analysis approach to bridge multiple scales within neuroscience. Frequency sliding is demonstrated in simulated neural networks and in human EEG data during a visual task. Simulations of biophysically detailed neuron models show that frequency sliding modulates spike threshold and timing variability, as well as coincidence detection. Finally, human resting-state EEG data demonstrate that frequency sliding occurs endogenously and can be used to identify large-scale networks. Frequency sliding appears to be a general principle that regulates brain function on multiple spatial and temporal scales, from modulating spike timing in individual neurons to coordinating large-scale brain networks during cognition and resting state.