Among the architectures and algorithms suggested for artificial neural networks, the Self-Organizing Map has the special property of effectively creating spatially organized "internal representations" of various features of input signals and their abstractions.
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SOMs are a type of neural network that can be used for dimensionality reduction and visualization of high-dimensional data. They work by training a grid of neurons, usually arranged in a hexagonal or rectangular grid, to represent the input data in a low-dimensional map. Each neuron in the SOM represents a prototype of the input data, and the neurons are organized in such a way that similar input data is mapped to nearby neurons in the grid.