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

Understand

Much has been learned about plasticity of biological synapses from empirical studies.

• Hebbian plasticity is driven by correlated activity of presynaptic and postsynaptic neurons.
• Synapses that converge onto the same neuron often behave as if they compete for a fixed resource; some survive the competition while others are eliminated.
• To provide computational interpretations of these aspects of synaptic plasticity, we formulate unsupervised learning as a zero-sum game between Hebbian excitation and anti-Hebbian inhibition in a neural network model.