Click on the image to see a PDF version (for zooming in)

Fig. 3.5. Self-organization of weight vectors. The weight vectors of two sample units are plotted on the receptor array at different stages of self-organization. The weight values are represented in gray-scale coding from white to black (low to high). Initially (iteration 0) the weights are uniformly randomly distributed; over several input presentations (such as those shown in Figure 3.4) the weights begin to resemble the input Gaussians in different locations of the receptor surface (iterations 1000, 5000, and 40,000). A neuron at the center of the network (top row) forms a Gaussian weight pattern at the center, while a neuron at the edge (bottom row) forms one near the edge. Such weight patterns together represent the topography of the input space, as seen in Figure 3.6.