Ensembles of Spiking Neural Networks

Understand

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.