Figure 2: Comparison of theoretical predictions (left column) and experimental observations (right column) in V1 and V2.

All theoretical predictions are generated using the same baseline parameters (Table 2). a, Theoretical mean firing rates as a function of stimulus contrast (V1: dashed line, V2: solid line). b, Experimental mean firing rates of V1 and V2 (data from [90] and [91], replotted for comparison). The shaded area with a solid border indicates the 25th to 75th percentile range for V1, and the one with the dashed border indicates the same for V2. c, Theoretical V1 power spectra at various stimulus contrast levels. Power spectra were normalized using the equation $\frac{(\text{power}-\text{baseline})}{(\text{power}+\text{baseline})}$, where baseline power is taken to be spontaneous activity at 0% contrast. The peak power shifts towards higher frequency with increasing contrast. The inset shows the $\frac{1}{f^4}$ power law decay at high frequency. d, Experimental power spectra from macaque V1 (data from [22], replotted for comparison). e, Theoretical coherence spectra between maximally firing neurons in V1 and V2. The peak coherence shifts towards higher frequency with increasing contrast. f, Experimental coherence spectra from macaque V1-V2 (data from [22], replotted for comparison). g, Theoretical communication subspaces, prediction performance as a function of dimensionality for inter-area (circles) and within-area (squares) communication. Simulation results indicate that the inter-area communication subspace is lower dimensional than the within-area communication. h, Experimental communication subspaces from macaque V1-V2 (data from [32], replotted for comparison). The plotted prediction performance in both panels g and h is an average across different subsets of the source and target neural populations, and the shaded areas represent the standard error of the mean (SEM).