Role of input correlations in shaping the variability and noise correlations of evoked activity in the neocortex.

Recent analysis of evoked activity recorded across different brain regions and tasks revealed a marked decrease in noise correlations and trial-by-trial variability. Given the importance of correlations and variability for information processing within the rate coding paradigm, several mechanisms have been proposed to explain the reduction in these quantities despite an increase in firing rates. These models suggest that anatomical clusters and/or tightly balanced excitation-inhibition can generate intrinsic network dynamics that may exhibit a reduction in noise correlations and trial-by-trial variability when perturbed by an external input. Such mechanisms based on the recurrent feedback crucially ignore the contribution of feedforward input to the statistics of the evoked activity. Therefore, we investigated how statistical properties of the feedforward input shape the statistics of the evoked activity. Specifically, we focused on the effect of input correlation structure on the noise correlations and trial-by-trial variability. We show that the ability of neurons to transfer the input firing rate, correlation, and variability to the output depends on the correlations within the presynaptic pool of a neuron, and that an input with even weak within-correlations can be sufficient to reduce noise correlations and trial-by-trial variability, without requiring any specific recurrent connectivity structure. In general, depending on the ongoing activity state, feedforward input could either increase or decrease noise correlation and trial-by-trial variability. Thus, we propose that evoked activity statistics are jointly determined by the feedforward and feedback inputs.