Stochastic transitions into silence cause noise correlations in cortical circuits

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 variability | noise correlations | brain state | auditory cortex | stochastic network dynamics N euronal 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). Th...

The spatial structure of correlated neuronal variability

“…We hypothesized that this inconsistency is explained by recent studies showing that much of the correlated variability measured in L2/3 arises from a low-dimensional shared source of latent variability 38,31,30,39,40 . We conjectured that this shared variability increases pairwise correlations in L2/3 at all distances, thereby “washing out” the negative correlations predicted by our theory.…”

“…However, a majority of population recordings in cortex reveal comparatively large correlations 25,26 . Several studies suggest that the magnitude of noise correlations is dependent on many factors 27 , including arousal 28 , attention 29 , anesthetic state 23,24,30,31 and cortical layer 32,33 . Finally, while in vivo whole cell recordings reveal strong positive e-e and i-i correlations coexisting with strong e-i correlations 13 , these correlation sources do not always perfectly cancel as predicted by some theoretical models 28 .…”

Inhibitory control of correlated intrinsic variability in cortical networks

“…When inhibition is weak, small deviations from the mean spike rate can be amplified by strong, non-specific, recurrent excitation into population-wide events (up states). These events produce strong adaptation currents in each activated neuron, which, in turn, result in periods of reduced spiking (down states) (Latham et al, 2000; Destexhe, 2009; Curto et al, 2009; Mochol et al, 2015). The alternations between up states and down states have an intrinsic periodicity given by the timescale of the adaptation currents, but the chaotic nature of the network adds an apparent randomness to the timing of individual events, thus creating intrinsic temporal variability.…”

“…Since large-scale fluctuations arise from the synchronization of adaptation currents across the population, reducing the strength of adaptation diminishes the fluctuations (Destexhe, 2009; Curto et al, 2009; Mochol et al, 2015). Increasing tonic input also diminishes large-scale fluctuations, but in a different way (Latham et al, 2000); when a subset of neurons receive increased tonic input, their adaptation currents may no longer be sufficient to silence them for prolonged periods, and the activity of these neurons during what would otherwise be a down state prevents the entire population from synchronizing.…”

Cortical Variability and Challenges for Modeling Approaches

“…Special consideration is currently given to understanding how spiking (Bujan et al, 2015; Deneve and Machens, 2016; Doiron et al, 2016; Hartmann et al, 2016; Landau et al, 2016) and phenomenological (Goris et al, 2014; Lin et al, 2015; Mochol et al, 2015; Arandia-Romero et al, 2016; Doiron et al, 2016) models account for the wide range of classical and new phenomena associated with trial-to-trial uncorrelated activity.…”