Learning spatiotemporal signals using a recurrent spiking network that discretizes time
This addresses the challenge of understanding how the brain learns and encodes sequential tasks using realistic computational models, though it appears incremental in advancing spiking network approaches.
The authors tackled the problem of learning spatiotemporal sequences using biologically plausible models by proposing a spiking recurrent network with a read-out layer trained to encode time and map it to other dimensions. They demonstrated that the model learns behaviorally relevant spatiotemporal dynamics and robustly replays sequences during 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.