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Much has been learned about plasticity of biological synapses from empiricalstudies. Hebbian plasticity is driven by correlated activity of presynaptic andpostsynaptic neurons. Synapses that converge onto the same neuron often behaveas if they compete for a fixed resource; some survive the competition whileothers are eliminated. To provide computational interpretations of theseaspects of synaptic plasticity, we formulate unsupervised learning as azero-sum game between Hebbian excitation and anti-Hebbian inhibition in aneural network model. The game formalizes the intuition that Hebbian excitationtries to maximize correlations of neurons with their inputs, while anti-Hebbianinhibition tries to decorrelate neurons from each other. We further include amodel of synaptic competition, which enables a neuron to eliminate allconnections except those from its most strongly correlated inputs. Throughempirical studies, we show that this facilitates the learning of sensoryfeatures that resemble parts of objects.

A correlation game for unsupervised learning yields computational  interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse  elimination