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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.
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