arXiv cs.NE
· Papers
Divisive Normalization Shapes Low-Rank Slow Manifolds for Continuous Working Memory
arXiv:2608.01947v1 Announce Type: cross Abstract: The ability to robustly maintain and update continuous variables is a hallmark of working memory. While classical continuous attractor networks suffer from severe fine-tuning fragility, standard artificial recurrent neural networks (RNNs) like GRUs and LSTMs typically f