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arXiv · 2006.14894

Biologically Plausible Learning of Text Representation with Spiking Neural Networks

Marcin Białas·Marcin Michał Mirończuk·Jacek Mańdziuk
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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 $80.19\%$ on the bydate version of the 20 newsgroups data set, which is a leading result amongst approaches that rely on low-dimensional text representations.

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Marcin Białas, Marcin Michał Mirończuk, Jacek Mańdziuk. 2020-06-26. Biologically Plausible Learning of Text Representation with Spiking Neural Networks. https://doi.org/10.1007/978-3-030-58112-1_30

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