Computation, rhythm, and cerebral slow waves

The trajectories of data and modelling from formerly divergent research efforts now seem to be converging to an unexpected region of the phase space of neuroscience. Computational network theory and simulation assume that temporal rhythm may be a significant parameter for the successful organization of nonlinear analog computation effected by hierarchical sets of biological neurons and of nonbiological circuitry alike. For neurobiology, the apparently chaotic rhythms of cerebral compound field potentials--the electroencephalogram (EEG) and slow waves of event related brain potentials (ERBP)--have long been a phenomenological embarrassment, of only marginal clinical utility. But recent data from molecular biophysics, nonlinear dynamics, artificial intelligence, and scalp-conducted human electrocorticography suggest a possible functional role in the serial gating of neural network computations for the familiar theta-alpha-beta rhythms of the EEG clinic.