Biologically Plausible Learning of Text Representation with Spiking Neural Networks
This provides a biologically inspired approach for text classification, but it is incremental as it builds on existing SNN and STDP techniques.
The study tackled the problem of generating low-dimensional text representations using biologically plausible spiking neural networks, achieving an accuracy of 80.19% on the 20 newsgroups dataset, which is a leading result for low-dimensional methods.
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.