Sparse deep predictive coding captures contour integration capabilities of the early visual system
Fig 4
Illustration of the procedure to generate interaction map.
In this illustrative example we consider a V1 representation with only 4 feature maps (represented in the upper-left box). Step 1 is to extract a neighborhood (of size 3x3 in the illustration only) around the most strongly activated neuron (represented with a red square in the illustration) for a given central preferred orientation (denoted θc). Step 2 is to normalize the neural activity in the extracted neighborhood using the marginal activity (see Eq 8). Step 3 is to compute the resulting orientation and activity at every position of the neighborhood using a circular mean (see Eqs 11 and 12 respectively). To keep a concise figure we have illustrated the computation of the central edge of the interaction map only. For simplification, the illustration shows only 1 neighborhood extraction whereas the interaction maps shown in the paper are computed by averaging neighborhoods centered on the 10 most strongly activated neurons.