Mochol, Gabriela and Hermoso-Mendizabal, Ainhoa and Sakata, Shuzo and Harris, Kenneth D. and de la Rocha, Jaime (2015) Stochastic transitions into silence cause noise correlations in cortical circuits. Proceedings of the National Academy

The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the co-activation of neurons due to anatomically shared inputs. Recent findings however indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here we investigate the hypothesis that correlations are dominated by neuronal co-inactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time-course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the micro-circuit to transiently cease firing. \body Introduction Neuronal noise correlations are defined as common fluctuations in the spiking activity of neurons under conditions of constant sensory input or motor output. Traditionally they have been thought to arise from the dense connectivity of the cortex, such that neighboring neurons sharing a fraction of their inputs should also share a fraction of their output variability (1). Several observations are consistent with this hypothesis: pair-wise correlations in the cortex decrease with cell pair distance (2) or with the difference in stimulus selectivity (3), dependencies that could follow from a variation in shared input given the anatomy of cortical circuits. Recent findings however challenge this simple interpretation. Recordings in the primate visual cortex have shown that attention or task context can change correlation structure (4–6) and that the magnitude of averaged correlation can be very low (7). In anesthetized rodents, correlations decrease with brain state desynchronization (8, 9) or when animals switch from quiet wakefulness to active whisking during waking (10). Moreover the commonly observed drop of spiking variability following stimulus onset (11–13) seems to occur jointly with a transient decrease in correlation (2, 14, 15). These observations suggest that correlations reflect the dynamical state of the circuit more than its hard-wired connectivity. Despite substantial progress in understanding the mechanisms giving rise to large individual variability in recurrent