Multiple Self-Organizing Maps (MSOM) is a type of architecture based on the use of several Self-Organizing Maps. These are built in a cascading fashion, so that you have multiple layers where each one uses the output information from the previous layers. In this way, as you move towards the end, the information takes on a higher level of meaning.
An artificial neural network based on the knowledge that the different areas of the brain, especially the cerebral cortex, have a topological organization and these areas perform specialized tasks: speech control and analysis of sensory signals (visual, auditory...).
MSOM discretizes the representation of the input space of the training samples, called a map. Self-organizing maps differ from other artificial neural networks because they apply competitive learning as opposed to error correction learning. MSOM uses a neighborhood function to preserve the topological properties of the input space.
Reference: Teuvo Kohonen. Self-Organized Formation of Topologically Correct Feature Maps..
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