Fig 3. Visualizing the most suppressive and facilitative surround.

A. Center surround similarity can be quantified by the feature correlation, in which we took a feature map in another reference CNN, in this case, layer 1 in ResNet50, and computed the correlation between the center features and the average of the surround features. More specifically, four locations (top, bottom, left, right) in the reference feature map that were closest to the middle between the gsf and the theoretical receptive field were used to get surround feature vectors. Feature correlation was calculated as the correlation coefficient between the mean of the four surround feature vectors and the center feature vector. B. Feature correlations in two CNNs. The shaded area indicates standard deviation. Asterisks indicate p value smaller than 0.05 in paired t-test. Note that the feature correlation depends on the selection of reference CNN. Feature correlations calculated with other reference CNNs can be found in S6 Fig. The most facilitative center (left image with no frame), most suppressive surround (middle image with cyan frames), and most facilitative surround (right image with pink frames) are shown for each selected neuron. C. Example neurons in early layers that have recognizable features: color (left column) and frequency (middle and right column). The most suppressive surrounds appeared similar to the center, whereas the most facilitative surrounds appeared different from the center. D. Example neurons in late layers that have more complex patterns.