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Knowledge about the collective dynamics of cortical spiking is veryinformative about the underlying coding principles. However, even most basicproperties are not known with certainty, because their assessment is hamperedby spatial subsampling, i.e. the limitation that only a tiny fraction of allneurons can be recorded simultaneously with millisecond precision. Building ona novel, subsampling-invariant estimator, we fit and carefully validate aminimal model for cortical spike propagation. The model interpolates betweentwo prominent states: asynchronous and critical. We find neither of them incortical spike recordings across various species, but instead identify a narrowreverberating regime. This approach enables us to predict yet unknownproperties from very short recordings and for every circuit individually,including responses to minimal perturbations, intrinsic network timescales, andthe strength of external input compared to recurrent activation - therebyinforming about the underlying coding principles for each circuit, area, stateand task.

Between perfectly critical and fully irregular: a reverberating model captures and predicts cortical spike propagation