Learning Spatiotemporal Signals Using A Recurrent Spiking Network That Discretizes Time

Author summary The brain has the ability to learn flexible behaviours on a wide range of time scales. Previous studies have successfully built spiking network models that learn a variety of computational tasks, yet often the learning involved is not biologically plausible. Here, we investigate a model that uses biological-plausible neurons and learning rules to learn a specific computational task: the learning of spatiotemporal sequences (i.e., the temporal evolution of an observable such as space, frequency or channel index). The model architecture facilitates the learning by separating the temporal information from the other dimension. The time component is encoded into a recurrent network that exhibits sequential dynamics on a behavioural time scale, and this network is then used as an engine to drive the read-out neurons that encode the spatial information (i.e., the second dimension). We demonstrate that the model can learn complex spatiotemporal spiking dynamics, such as the song of a bird, and replay the song robustly spontaneously.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.