The ghost mechanism derives a 1D canonical model of abrupt learning in RNNs from ghost points of saddle-node bifurcations, predicting an inverse-power-law critical learning rate and gradient-based failure modes.
Neu- rons in a forebrain nucleus required for vocal plasticity rapidly switch between precise firing and variable burst- ing depending on social context
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A ghost mechanism: An analytical model of abrupt learning in recurrent networks
The ghost mechanism derives a 1D canonical model of abrupt learning in RNNs from ghost points of saddle-node bifurcations, predicting an inverse-power-law critical learning rate and gradient-based failure modes.