Before revision:  The dynamics of the  recurrent network is constrained to encode time while the read-out neurons encode space. Space is then linked with time through Hebbian learning. Here we demonstrate that the model is able to learn spatiotemporal dynamics on a timescale that is behaviorally relevant . Learned sequences are robustly replayed during a regime of spontaneous activity.
After revision:  The dynamics of the recurrent network is constrained to encode time while the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on a timescale that is behaviorally relevant . Learned sequences are robustly replayed during a regime of spontaneous activity.