Editing Unsupervised learning (section)

=== Hebbian Learning, ART, SOM ===

The classical example of unsupervised learning in the study of neural networks is [[Donald Hebb]]'s principle, that is, neurons that fire together wire together.<ref name="Buhmann" /> In [[Hebbian learning]], the connection is reinforced irrespective of an error, but is exclusively a function of the coincidence between action potentials between the two neurons.<ref name="Comesana" /> A similar version that modifies synaptic weights takes into account the time between the action potentials ([[spike-timing-dependent plasticity]] or STDP). Hebbian Learning has been hypothesized to underlie a range of cognitive functions, such as [[pattern recognition]] and experiential learning.

Among [[Artificial neural network|neural network]] models, the [[self-organizing map]] (SOM) and [[adaptive resonance theory]] (ART) are commonly used in unsupervised learning algorithms. The SOM is a topographic organization in which nearby locations in the map represent inputs with similar properties. The ART model allows the number of clusters to vary with problem size and lets the user control the degree of similarity between members of the same clusters by means of a user-defined constant called the vigilance parameter. ART networks are used for many pattern recognition tasks, such as [[automatic target recognition]] and seismic signal processing.<ref name="Carpenter" />