Synaptic competition

Constraints or interactions make the strengthening of some inputs limit others. In developmental models, competition together with input correlations can segregate ON and OFF subregions or inputs from the two eyes; correlations alone do not specify the full biological developmental mechanism.

A learning rule can constrain the sum or norm of incoming synaptic weights to prevent unbounded Hebbian learning. For ${\sum }_i{w}_{ai}=S$, subtracting the mean unconstrained update from each update preserves the sum. Bounds and renormalization may also be needed to keep excitatory weights nonnegative. Normalization of learning weights is distinct from divisive gain control in instantaneous neural responses.