  This paper demonstrates how to construct ensembles of spiking neural networks
producing state-of-the-art results, achieving classification accuracies of
98.71%, 100.0%, and 99.09%, on the MNIST, NMNIST and DVS Gesture datasets
respectively. Furthermore, this performance is achieved using simplified
individual models, with ensembles containing less than 50% of the parameters of
published reference models. We provide comprehensive exploration on the effect
of spike train interpretation methods, and derive the theoretical methodology
for combining model predictions such that performance improvements are
guaranteed for spiking ensembles. For this, we formalize spiking neural
networks as GLM predictors, identifying a suitable representation for their
target domain. Further, we show how the diversity of our spiking ensembles can
be measured using the Ambiguity Decomposition. The work demonstrates how
ensembling can overcome the challenges of producing individual SNN models which
can compete with traditional deep neural networks, and creates systems with
fewer trainable parameters and smaller memory footprints, opening the door to
low-power edge applications, e.g. implemented on neuromorphic hardware.
