Linear Fisher information after quadratic nonlinearity, unstructured weights. In contrast to Figure 4, panels a and b are plotted on a log scale. (a) Linear Fisher information as a function of the mean, $μ$⁠, of the log-normal distribution used to draw the common noise synaptic weights. Solid lines denote means, while shaded regions denote one standard deviation across the 1000 drawings of weights from the log-normal distribution. (b) Same as panel a but for networks in which private variability dominates (⁠$σP=5$⁠, $σC=1$⁠). (c) Normalized linear Fisher information. Same plot as Figure 4c, but the average Fisher information across the 1000 samples is normalized across $μ$ (akin to normalizing across $kw$⁠).