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The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns.
This is partly motivated by how visual, auditory or other sensory information is handled in separate parts of the cerebral cortex in the human brain.
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An illustration of the training of a self-organizing map.
The blue blob is the distribution of the training data, and the small white disc is the current training datum drawn from that distribution.
A self-organizing map consists of components called nodes or neurons.
Associated with each node are a weight vector of the same dimension as the input data vectors, and a position in the map space.