SOM (pronounced as ess-o-em) is another interesting ANN model that is useful for unsupervised learning. SOMs are used in several practical applications such as handwriting and image recognition. We will also revisit SOMs when we discuss clustering in Chapter 7, Clustering Data.

In unsupervised learning, the sample data contains no expected output values, and the ANN must recognize and match patterns from the input data entirely on its own. SOMs are used for competitive learning, which is a special class of unsupervised learning in which the neurons in the output layer of the ANN compete among themselves for activation. The activated neuron determines the final output value of the ANN, and hence, the activated neuron is also termed as a winning neuron.

Neurobiological studies have shown that different sensory inputs sent to the brain are mapped to the corresponding areas of the brain's cerebral cortex in an orderly pattern. Thus, neurons that deal with closely related operations...