| Abstract: | The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation 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 coinactivation: 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 microcircuit to transiently cease firing.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: pairwise 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 hardwired connectivity.Despite substantial progress in understanding the mechanisms giving rise to large individual variability in recurrent networks (9, 16–18), we still lack a canonical model that can generate correlations with the same magnitude and spatiotemporal structure as those observed in cortical circuits. Balanced networks, for instance, a common model that reproduces the large variability of cortical neurons (9, 18, 19), show near-zero averaged correlations (9). Numerous studies have investigated the generation of synchronous firing (20), but whether short bursts of population activity can quantitatively account for the spike count correlations found in the data is unclear. Recurrent networks can also generate fast oscillations in the population activity, but, in a regime of low rates, typical of cortical circuits, average spike count correlations are negligible (21). Network models producing nonzero average correlations are those exhibiting up and down dynamics (22–29). Most of these studies have focused on investigating the mechanisms underlying the slow oscillatory activity observed in cortical slices (30), under anesthesia (31, 32), or during slow-wave sleep (33). Only recently the impact of up and down switching on trial-to-trial response variability (25) and on the probability distribution of multiunit activity (29) across brain states has been investigated. Whether the alternation between up and down phases could quantitatively account for the pairwise correlations observed in different brain states and describe their stimulus-evoked dynamics remains an open question.To investigate the mechanisms producing correlated firing, we recorded the spiking activity of large populations of neurons from the auditory cortex of anesthetized rats. During spontaneous activity, changes in correlation were largely explained by variation of the occurrence rate of periods during which neurons in the circuit stopped firing. Furthermore, the time course of correlation in response to an acoustic stimulus reflected the transient variation of this silence density. A computational rate model with fluctuation-driven transitions between silent and active attractors could explain the experimentally observed time course of correlation and its relation to silence density. Our findings suggest that the dynamics of these transitions play a fundamental role in generating noise correlations among cortical neurons. |
