Learning to produce spatiotemporal sequences is a common task  the brain has to solve. While many sequential behavioursdiffer superficially, the URLanization 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.
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.
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While many sequential behavioursdiffer superficially, the URLanization of the computation might be similar
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encode space. Space is then linked with time through Hebbian learning. Here we
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