For supragranular and infragranular layers we used a distribution of σE = 15° for

local intracortical excitatory inputs, σI = 40° for local intracortical inhibitory inputs, and σE = 20° for feedforward iso-oriented inputs (from granular and supragranular layers, respectively; see Supplemental Experimental Procedures). For the granular layer we used σE = 30° for local intracortical excitatory inputs, σI = 40° for local intracortical inhibitory inputs, and σE = 20° for the inputs from infragranular layers. As shown in Figure 6A, the specific structure of synaptic connectivity within and between layers ensures model mean noise correlations RG7204 order in the 0°–30° orientation range that were highly consistent with experimental data for each layer. Indeed, despite the fact see more that granular layer neurons receive highly correlated inputs from the infragranular layer, the structure of synaptic connectivity, i.e., the broad tuning of excitatory intracortical inputs, decorrelates responses. In turn, neurons in supragranular

layers, where the tuning of excitatory intracortical inputs is narrower, show an increase in correlated variability although their inputs are only weakly correlated. The noise correlation values obtained using our three-layer model are consistent with those obtained experimentally, i.e., correlations in supragranular and infragranular layers are significantly greater than those in the granular layer (one-way ANOVA, F (2, 115,638) = 72,346.16, p < 0.00001; post hoc multicomparison, Tukey’s least significant difference), but statistically indistinguishable between each other (p > 0.05). Do laminar difference in noise correlations influence the information encoded in population activity in each layer? A measure of the accuracy

of population coding is the network discrimination threshold (inversely proportional to the square Mannose-binding protein-associated serine protease root of Fisher information) (Abbott and Dayan, 1999), which we computed by using a linear decoder of stimulus orientation (Seriès et al., 2004; Chelaru and Dragoi, 2008). Orientation discrimination performance for each layer was estimated by decoding the spike counts in each layer obtained when bar stimuli of two nearby orientations (90° and 92°) were presented for 504 trials of 0.5 s each (Figure 6B). The decoder was trained to maximize the Fisher information of population responses (Abbott and Dayan, 1999; Seriès et al., 2004; Chelaru and Dragoi, 2008) and, as a result, minimize the discrimination threshold between two adjacent stimulus orientations (Supplemental Experimental Procedures).