  Event-based machine learning promises more energy-efficient AI on future
neuromorphic hardware. Here, we investigate how the recently discovered
Eventprop algorithm for gradient descent on exact gradients in spiking neural
networks can be scaled up to challenging keyword recognition benchmarks. We
implemented Eventprop in the GPU-enhanced Neural Networks framework and used it
for training recurrent spiking neural networks on the Spiking Heidelberg Digits
and Spiking Speech Commands datasets. We found that learning depended strongly
on the loss function and extended Eventprop to a wider class of loss functions
to enable effective training. When combined with the right additional
mechanisms from the machine learning toolbox, Eventprop networks achieved
state-of-the-art performance on Spiking Heidelberg Digits and good accuracy on
Spiking Speech Commands. This work is a significant step towards a low-power
neuromorphic alternative to current machine learning paradigms.
