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Paper details
Number 3 - September 2025
Volume 35 - 2025
Iteration over event space in time-to-first-spike spiking neural networks for Twitter (𝕏) bot classification
Mateusz Pabian, Dominik Rzepka, Mirosław Pawlak
Abstract
This study proposes a variant of a time-coding time-to-first-spike spiking neural network (SNN) model with its neurons
capable of generating spike trains in response to observed event sequences. This extends an existing model that is limited
to generating and observing at most one event per synapse. 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 for event sequence
data. The model is trained and evaluated on a Twitter (𝕏) 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
preprocessing steps and training hyperparameter choice on model classification accuracy is analyzed in an ablation study.
Keywords
spiking neural networks, event-based computing, bot detection, supervised learning