Figure 2
From: Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks
Creating topographical correlation maps. We extract the 3D activation patterns from the network convolutional layers. The first 2 Dimensions have a spatial relation with the image space (width and height). At each (x, y) position in feature maps, we extract a pattern vector with the length equivalent to the depth and construct the RDM matrix from the neural network activity patterns at each (x, y) location. Comparison of these RDM matrices with a brain ROI RDM results in a 2D correlation map which we then up-sample it to the image size (topographical map). The pictures used in this figure are not examples of the stimulus set due to copyright.
