  This study proposes a framework that extends existing time-coding
time-to-first-spike spiking neural network (SNN) models to allow processing
information changing over time. We explain spike propagation through a model
with multiple input and output spikes at each neuron, as well as design
training rules for end-to-end backpropagation. This strategy enables us to
process information changing over time. The model is trained and evaluated on a
Twitter bot detection task where the time of events (tweets and retweets) is
the primary carrier of information. This task was chosen to evaluate how the
proposed SNN deals with spike train data composed of hundreds of events
occurring at timescales differing by almost five orders of magnitude. The
impact of various parameters on model properties, performance and training-time
stability is analyzed.
