Response outcomes gate the impact of expectations on perceptual decisions.

Perceptual decisions are based on sensory information but can also be influenced by expectations built from recent experiences. Can the impact of expectations be flexibly modulated based on the outcome of previous decisions? Here, rats perform an auditory task where the probability to repeat the previous stimulus category is varied in trial-blocks. All rats capitalize on these sequence correlations by exploiting a transition bias: a tendency to repeat or alternate their previous response using an internal estimate of the sequence repeating probability. Surprisingly, this bias is null after error trials. The internal estimate however is not reset and it becomes effective again after the next correct response. This behavior is captured by a generative model, whereby a reward-driven modulatory signal gates the impact of the latent model of the environment on the current decision. These results demonstrate that, based on previous outcomes, rats flexibly modulate how expectations influence their decisions.

Stochastic transitions into silence cause noise correlations in cortical circuits.

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.