I ran some more tests, this time training topographic maps on a set of 3D models, including a hand, a human head, a wooden doll, a sword, and a full human body. Each image below is a sequence captured during the training process for each model. One interesting detail is that the maps learn to devote more resources to the regions that are sampled most often, which correspond to the parts of the models with lots of vertices. For example, the human head model has a lot of vertices in the mouth cavity, so it becomes well-represented in the topographic map.
This is the basic idea behind the homunculus representation of the sensory and motor cortices: the brain regions that represent the body learn to devote more real estate to those areas that need high resolution representations. Check out this image from the wikipedia article:
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