Biologically Plausible Learning of Text Representation with Spiking Neural Networks
arXiv:2006.14894 · doi:10.1007/978-3-030-58112-1_30
Abstract
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 on the bydate version of the 20 newsgroups data set, which is a leading result amongst approaches that rely on low-dimensional text representations.
This article was originally submitted for Parallel Problem Solving from Nature conference and will be available in Springer Lecture Notes in Computer Science (LNCS)