  In this work, we explore a new Spiking Neural Network (SNN) formulation with
Resonate-and-Fire (RAF) neurons (Izhikevich, 2001) trained with gradient
descent via back-propagation. The RAF-SNN, while more biologically plausible,
achieves performance comparable to or higher than conventional models in the
Machine Learning literature across different network configurations, using
similar or fewer parameters. Strikingly, the RAF-SNN proves robust against
noise induced at testing/training time, under both static and dynamic
conditions. Against CNN on MNIST, we show 25% higher absolute accuracy with
N(0, 0.2) induced noise at testing time. Against LSTM on N-MNIST, we show 70%
higher absolute accuracy with 20% induced noise at training time.
