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Fig. 8.5. Effect of different input streams and initial organizations on the self-organizing process. Using a different stream of random numbers for the weights (top two rows) results in different initial maps of orientation preference (a), but has almost no effect on the final self-organized maps (c), nor the lateral connections in them. (The lateral connections are shown in white outline for one sample neuron, marked with a small white square; orientation selectivity is not plotted in this Figure to make the preferences visible in the initial map.) The final result is the same because lateral excitation smooths out differences in the initial weight values, and leads to similar large-scale patterns of activation at each iteration. This process can be seen in the early map (b): The same large-scale features are emerging in both maps despite locally different patterns of noise caused by the different initial weights. In contrast, changing the input stream (bottom two rows) produces very different early and final map patterns and lateral connections, even when the initial weights are identical. Thus, the input patterns are the crucial source of variation, not the initial weights. An animated demo of these examples can be seen at ...