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Fig. 10.5. Self-organization of the FSA map. The PGO activation is shown in gray scale from black to white (low to high), and the V1 and FSA activities and the afferent and lateral weights in gray scale from white to black (low to high). (a) Each input pattern consisted of two dark three-dot configurations with random nearly vertical orientations presented at random locations on the PGO sheet. (b) The V1 neurons compute their responses based on this input, relayed through the LGN. (c) FSA neurons initially respond to any activity in their receptive fields, but after training (d), only neurons with closely matching RFs respond. In the FSA plots, the inner square represents the FSA and is drawn to scale with the retina. The outer square is provided to help locate the FSA responses on the retina, as was done in Figures 4.4 and A.1a. Through self-organization, the FSA neurons develop RFs selective for a range of V1 activity patterns like those resulting from the three-dot stimuli (e and f , drawn in the same scale as b for two sample neurons). The RFs are patchy because the weights target specific orientation patches in V1. This match between the FSA and the local self-organized pattern in V1 would be difficult to ensure without training on internally generated patterns. The FSA neurons also develop lateral inhibitory connections with a smooth Gaussian profile (g and h, drawn in the same scale as c and d for the two neurons in e and f ). Plots (i) and (j) show the afferent weights for every third neuron in the FSA. All neurons develop roughly similar weight profiles, differing primarily by the position of their preferred stimuli on the retina and by the specific orientation patches targeted in V1. The largest differences between RFs are along the outside border, where the neurons are less selective for three-dot patterns. Overall, the FSA develops into a face detection map, signaling the location of facelike stimuli.