A ghost mechanism: An analytical model of abrupt learning in recurrent networks

ArXi:2501.02378v2 Announce Type: replace Abrupt learning is a common phenomenon in recurrent neural networks (RNNs) trained on working memory tasks. In such cases, the networks develop transient slow regions in state space that extend the effective timescales of computation. However, the mechanisms driving sudden performance improvements and their causal role remain unclear. To address this gap, we