Learning spatiotemporal signals using a recurrent spiking network that discretizes time

Learning to produce spatiotemporal sequences is a common task the brain has to solve. While many sequential behaviours differ superficially, the underlying organization of the computation might be similar. The way the brain learns these tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network drives a read-out layer. Plastic synapses follow common Hebbian learning rules. 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.