Neural Comput - Simple modification of Oja rule limits L1-norm of weight vector and leads to sparse connectivity.

Tópicos

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Resumo

This letter describes a simple modification of the Oja learning rule, which asymptotically constrains the L1-norm of an input weight vector instead of the L2-norm as in the original rule. This constraining is local as opposed to commonly used instant normalizations, which require the knowledge of all input weights of a neuron to update each one of them individually. The proposed rule converges to a weight vector that is sparser (has more zero weights) than the vector learned by the original Oja rule with or without the zero bound, which could explain the developmental synaptic pruning.

Resumo Limpo

letter describ simpl modif oja learn rule asymptot constrain lnorm input weight vector instead lnorm origin rule constrain local oppos common use instant normal requir knowledg input weight neuron updat one individu propos rule converg weight vector sparser zero weight vector learn origin oja rule without zero bound explain development synapt prune

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