Learning using Dynamical Regime Identification and Synchronization

This study proposes to generalize Hebbian learning by identifying and synchronizing the dynamical regimes of individual nodes in a recurrent network. The connection weights are updated according to the closeness in the observed local dynamical regimes. Demonstration of the viability of this method is provided on spiking recurrent neural networks. Experiments are made with both artificial and real continuous data, using a frequency population coding.