  Spiking neural networks have shown much promise as an energy-efficient
alternative to artificial neural networks. However, understanding the impacts
of sensor noises and input encodings on the network activity and performance
remains difficult with common neuromorphic vision baselines like
classification. Therefore, we propose a spiking neural network approach for
single object localization trained using surrogate gradient descent, for frame-
and event-based sensors. We compare our method with similar artificial neural
networks and show that our model has competitive/better performance in
accuracy, robustness against various corruptions, and has lower energy
consumption. Moreover, we study the impact of neural coding schemes for static
images in accuracy, robustness, and energy efficiency. Our observations differ
importantly from previous studies on bio-plausible learning rules, which helps
in the design of surrogate gradient trained architectures, and offers insight
to design priorities in future neuromorphic technologies in terms of noise
characteristics and data encoding methods.
