  This study adapts the highly-versatile siamese neural network model to the
event data domain. We introduce a supervised training framework for optimizing
Earth Mover's Distance (EMD) between spike trains with spiking neural networks
(SNN). We train this model on images of the MNIST dataset converted into
spiking domain with novel conversion schemes. The quality of the siamese
embeddings of input images was evaluated by measuring the classifier
performance for different dataset coding types. The models achieved performance
similar to existing SNN-based approaches (F1-score of up to 0.9386) while using
only about 15% of hidden layer neurons to classify each example. Furthermore,
models which did not employ a sparse neural code were about 45% slower than
their sparse counterparts. These properties make the model suitable for low
energy consumption and low prediction latency applications.
