  Spiking neural networks (SNNs) have become an interesting alternative to
conventional artificial neural networks (ANN) thanks to their temporal
processing capabilities and energy efficient implementations in neuromorphic
hardware. However the challenges involved in training SNNs have limited their
performance in terms of accuracy and thus their applications. Improving
learning algorithms and neural architectures for a more accurate feature
extraction is therefore one of the current priorities in SNN research. In this
paper we present a study on the key components of modern spiking architectures.
We design a spiking version of the successful residual network architecture and
provide an in-depth study on the possible implementations of spiking residual
connections. This study shows how, depending on the use case, the optimal
residual connection implementation may vary. Additionally, we empirically
compare different techniques in image classification datasets taken from the
best performing networks. Our results provide a state of the art guide to SNN
design, which allows to make informed choices when trying to build the optimal
visual feature extractor. Finally, our network outperforms previous SNN
architectures in CIFAR-10 (94.14%) and CIFAR-100 (74.65%) datasets and matches
the state of the art in DVS-CIFAR10 (72.98%), with less parameters than the
previous state of the art and without the need for ANN-SNN conversion. Code
available at https://github.com/VicenteAlex/Spiking_ResNet
