  Learning to produce spatiotemporal sequences is a common task that the brain
has to solve. The same neural substrate may be used by the brain to produce
different sequential behaviours. The way the brain learns and encodes such
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 of excitatory and inhibitory biophysical neurons
drives a read-out layer: the dynamics of the driver recurrent network is
trained to encode time which is then mapped through 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 time scales that are
behaviourally relevant and we show that the learned sequences are robustly
replayed during a regime of spontaneous activity.
