  This study proposes a novel biologically plausible mechanism for generating
low-dimensional spike-based text representation. First, we demonstrate how to
transform documents into series of spikes spike trains which are subsequently
used as input in the training process of a spiking neural network (SNN). The
network is composed of biologically plausible elements, and trained according
to the unsupervised Hebbian learning rule, Spike-Timing-Dependent Plasticity
(STDP). After training, the SNN can be used to generate low-dimensional
spike-based text representation suitable for text/document classification.
Empirical results demonstrate that the generated text representation may be
effectively used in text classification leading to an accuracy of $80.19\%$ on
the bydate version of the 20 newsgroups data set, which is a leading result
amongst approaches that rely on low-dimensional text representations.
