Abstract: Intuitive and easy to use application programming interfaces such as Keras have played a large part in the rapid acceleration of ANN-based machine learning. We want to unlock the potential of spike-based machine learning in the same way, so here we present mlGeNN, an easy way to define, train and test spiking neural networks using GeNN — our efficient GPU-accelerated SNN simulator. Using GeNN, we demonstrate that we can use e-prop to train recurrent SNN classifiers on datasets including the Spiking Heidelberg Digits (SHD) and DVS gesture. We show that these classifiers can not only offer comparable performance to LSTMs but are up to 7× faster when performing inference on the same GPU hardware. As GeNN is designed to exploit sparse connectivity, by replacing the dense recurrent connectivity in classifier models with random sparse connectivity, we can reduce the time taken to train such models by almost 10× — although this results in some reduction in classification accuracy. However, in biological brains, alongside the changes to the strength of existing synapses driven by synaptic plasticity, structural plasticity prunes unused synapses and forms new ones. The Deep-R learning rule provides a framework for combining gradient-based learning with structural plasticity and by combining Deep-R with e-prop, we demonstrate that the aforementioned reduction in classification accuracy can be eliminated, even in very sparsely connected models.