Correlated Variability in Laminar Cortical Circuits

Abstract

SUMMARY 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.

Cortical computation in mammals and birds

“…Understanding the structure of their activity therefore requires recording from many neurons simultaneously. Recent recordings from primate visual cortex indicate that "noise correlations," which indicate that population activity shows coordination beyond that imposed by sensory stimuli, are weakest in layer 4, and stronger in sub-and supragranular layers (16,17). Consistently, Calabrese and Woolley (4) find that correlations are weakest in field L2.…”

High noise correlation between the functionally connected neurons in emergent V1 microcircuits

“…Many investigators (Zohary et al 1994;Graf et al 2011;Hansen et al 2012;Cossell et al 2015) have suggested that high Rsc is directly related to the strength of the connection between neurons. We thus investigated how the probability of the peak (P) in the CCG might be related to Rsc between the same neurons.…”

“…Investigators have also reported in V1 that, a CCG between a neuronal pair fluctuates systematically with the stimulus (orientation) irrespective of low or high Rsc between them (Gawne et al 1996;Bair et al 2001;Reich et al 2001;Kohn and Smith 2005). Thus, a contentious debate persists concerning the precise nature of Rsc in microcircuits (Cohen and Kohn 2011;Averbeck et al 2006) A major factor while calculating Rsc is the counting window (resolution-window, as we name it) that is employed to compute Rsc (Cohen and Kohn 2011;Hansen et al 2012;Schulz et al 2015). Shorter windows may underestimate the true Rsc between neurons, whereas bigger windows may add artificial correlation between spike-trains (Cohen and Kohn 2011;Hansen et al 2012).…”

“…Thus, a contentious debate persists concerning the precise nature of Rsc in microcircuits (Cohen and Kohn 2011;Averbeck et al 2006) A major factor while calculating Rsc is the counting window (resolution-window, as we name it) that is employed to compute Rsc (Cohen and Kohn 2011;Hansen et al 2012;Schulz et al 2015). Shorter windows may underestimate the true Rsc between neurons, whereas bigger windows may add artificial correlation between spike-trains (Cohen and Kohn 2011;Hansen et al 2012). …”

Paying Attention to the Cortical Layers

“…It is also of interest to compare the laminar profile of noise correlations in V4, with the strongest correlations in the input layers, to the laminar profile previously found in V1, where correlations in the input layers are close to zero (Hansen et al, 2012). Apparently, spike-count correlations do not form canonical laminar patterns throughout the visual system but vary from area to area instead.…”