  Inspired by more detailed modeling of biological neurons, Spiking neural
networks (SNNs) have been investigated both as more biologically plausible and
potentially more powerful models of neural computation, and also with the aim
of extracting biological neurons' energy efficiency; the performance of such
networks however has remained lacking compared to classical artificial neural
networks (ANNs). Here, we demonstrate how a novel surrogate gradient combined
with recurrent networks of tunable and adaptive spiking neurons yields
state-of-the-art for SNNs on challenging benchmarks in the time-domain, like
speech and gesture recognition. This also exceeds the performance of standard
classical recurrent neural networks (RNNs) and approaches that of the best
modern ANNs. As these SNNs exhibit sparse spiking, we show that they
theoretically are one to three orders of magnitude more computationally
efficient compared to RNNs with comparable performance. Together, this
positions SNNs as an attractive solution for AI hardware implementations.
