Leaky RNNs improve grid-cell-like representations and path-integration accuracy by acting as a low-pass filter that stabilizes dynamics against noise.
The regularization term is defined asL w =λ∥W rec∥2, which helps prevent the model from over fitting to specific features and improves its general- ization capability
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Impact of leaky dynamics on predictive path integration accuracy in recurrent neural networks
Leaky RNNs improve grid-cell-like representations and path-integration accuracy by acting as a low-pass filter that stabilizes dynamics against noise.