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Fig. 5.9. Self-organization of the orientation map. The orientation preference and selectivity of each neuron was computed before (top row) and after selforganization (bottom row). The preferences are color coded and selectivity represented in gray scale as in Figure 2.4. (a) The orientation preferences were initially random, but over self-organization, the network developed a smoothly varying orientation map. The map contains all the features found in animal maps, such as linear zones, pairs of pinwheels, saddle points, and fractures (outlined as in Figure 2.4). (b) Before self-organization, the neurons are unselective (i.e. dark), but nearly all of the self-organized neurons are highly selective (light). (c) Overlaying the orientation and selectivity plots (by representing selectivity with color saturation as in Figure 5.7) shows that regions of low selectivity in the self-organized map tend to occur near pinwheel centers and along fractures. (d) Histograms of the number of neurons preferring each orientation (OR H) are essentially flat because the initial weight patterns were random, the training inputs included all orientations equally, and LISSOM does not have artifacts that would bias its preferences. These plots show that LISSOM can develop biologically realistic orientation maps through self-organization based on abstract input patterns. An animated demo of the self-organizing process can be seen at ...