# Learning spatiotemporal signals using a recurrent spiking network that discretizes time

## TL;DR

This paper introduces a biologically plausible spiking recurrent neural network model that learns to encode and reproduce complex spatiotemporal sequences, demonstrating robustness and relevance to behavior.

## Contribution

It presents a novel biologically plausible model that learns spatiotemporal sequences using Hebbian rules in a spiking recurrent network.

## Key findings

- The model can learn behaviorally relevant spatiotemporal dynamics.
- Learned sequences are robustly replayed during spontaneous activity.
- The approach encodes time and space using biologically realistic neurons.

## Abstract

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.