#!/lusr/bin/php Demos: Computational Maps in the Visual Cortex
    Computational Maps in the Visual Cortex
     Demo 5.9
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OR key
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(a) OR preference (b) OR selectivity (c) OR preference &
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Demo 5.9. Self-organization of the OR map. This animation of Figure 5.9 shows how the LISSOM OR map self-organizes over time. The OR preference, OR selectivity, and combined preference and selectivity of each neuron in the LISSOM OR map are shown over 10,000 input presentations (oriented Gaussian patterns). The OR preference is color coded according to the key on top, and selectivity represented in gray scale from black to white (low to high), as in the macaque OR map of 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 in Figure 5.9). (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 i.e. its fullness or intensity) 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.

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