Oja's Rule
An improved variant of the Hebbian learning rule that achieves normalization by introducing weight decay, addressing the instability of the pure Hebbian rule in which positive feedback causes weights to grow without bound.
Timeline (last 90 days)
Hebbian learning and its variants (such as the Oja rule, BCM rule, and STDP) usually still assume that plasticity signals are continuously available.
To improve the stability of the Hebbian rule, researchers proposed the Oja rule (introducing weight decay to achieve normalization) and the BCM rule (introducing a sliding modification threshold).
All Facts (2)
Hebbian learning and its variants (such as the Oja rule, BCM rule, and STDP) usually still assume that plasticity signals are continuously available.
50%UnverifiedTo improve the stability of the Hebbian rule, researchers proposed the Oja rule (introducing weight decay to achieve normalization) and the BCM rule (introducing a sliding modification threshold).
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