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Brain electric field potentials are dominated by an arrhythmic broadband signal, but the underlying mechanism is poorly understood. Here we propose that broadband power spectra characterize recurrent neural networks of nodes (neurons or clusters of neurons), endowed with an effective balance between excitation and inhibition tuned to keep the network on the edge of dynamical instability. These networks show a fast mode reflecting local dynamics and a slow mode emerging from distributed recurrent connections. Together, the 2 modes produce power spectra similar to those observed in human intracranial EEG (i.e., electrocorticography, ECoG) recordings. Moreover, such networks convert spatial input correlations across nodes into temporal autocorrelation of network activity. Consequently, increased independence between nodes reduces low-frequency power, which may explain changes observed during behavioral tasks. Lastly, varying network coupling causes activity changes that resemble those observed in human ECoG across different arousal states. The model links macroscopic features of empirical ECoG power to a parsimonious underlying network structure, and suggests mechanisms for changes observed across behavioral and arousal states. This work provides a computational framework to generate and test hypotheses about cellular and network mechanisms underlying whole brain electrical dynamics, their variations across states, and potential alterations in brain diseases.

Random Recurrent Networks Near Criticality Capture the Broadband Power Distribution of Human ECoG Dynamics