  Research around Spiking Neural Networks has ignited during the last years due
to their advantages when compared to traditional neural networks, including
their efficient processing and inherent ability to model complex temporal
dynamics. Despite these differences, Spiking Neural Networks face similar
issues than other neural computation counterparts when deployed in real-world
settings. This work addresses one of the practical circumstances that can
hinder the trustworthiness of this family of models: the possibility of
querying a trained model with samples far from the distribution of its training
data (also referred to as Out-of-Distribution or OoD data). Specifically, this
work presents a novel OoD detector that can identify whether test examples
input to a Spiking Neural Network belong to the distribution of the data over
which it was trained. For this purpose, we characterize the internal
activations of the hidden layers of the network in the form of spike count
patterns, which lay a basis for determining when the activations induced by a
test instance is atypical. Furthermore, a local explanation method is devised
to produce attribution maps revealing which parts of the input instance push
most towards the detection of an example as an OoD sample. Experimental results
are performed over several image classification datasets to compare the
proposed detector to other OoD detection schemes from the literature. As the
obtained results clearly show, the proposed detector performs competitively
against such alternative schemes, and produces relevance attribution maps that
conform to expectations for synthetically created OoD instances.
