A sparsely connected associative memory model is built with small-world connectivity, and trained on both random, and real-world image sets. It is found that pattern recall using real-world images can vary significantly from that of random images, and that the relationship between network wiring strategy and performance changes dramatically when training sets consist of certain types of real-world image.
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