Correlated variability in laminar cortical circuits.

Despite the fact that strong trial-to-trial correlated variability in responses has been reported in many cortical areas, recent evidence suggests that neuronal correlations are much lower than previously thought. Here, we used multicontact laminar probes to revisit the issue of correlated variability in primary visual (V1) cortical circuits. We found that correlations between neurons depend strongly on local network context--whereas neurons in the input (granular) layers showed virtually no correlated variability, neurons in the output layers (supragranular and infragranular) exhibited strong correlations. The laminar dependence of noise correlations is consistent with recurrent models in which neurons in the granular layer receive intracortical inputs from nearby cells, whereas supragranular and infragranular layer neurons receive inputs over larger distances. Contrary to expectation that the output cortical layers encode stimulus information most accurately, we found that the input network offers superior discrimination performance compared to the output networks.