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Leveraging spiking neural networks for topic modeling.

Marcin Białas¹, Marcin Michał Mirończuk¹, Jacek Mańdziuk²

¹National Information Processing Institute, al. Niepodległości 188b, 00-608, Warsaw, Poland.

Neural Networks : the Official Journal of the International Neural Network Society

|July 7, 2024

Summary

This summary is machine-generated.

Spiking neural networks (SNNs) can effectively perform topic modeling (TM) by learning word patterns. A novel Spiking Topic Model (STM) demonstrates competitive performance against established methods in unsupervised natural language processing.

Keywords:

STDP Spiking neural network Spiking topic model Topic modeling Unsupervised learning

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Area of Science:

Artificial Intelligence
Computational Linguistics
Neuroscience

Background:

Topic modeling (TM) identifies latent themes in large text corpora.
Traditional TM methods often rely on statistical or embedding-based approaches.
Spiking neural networks (SNNs) offer a biologically inspired alternative for pattern recognition.

Purpose of the Study:

To investigate the efficacy of SNNs, specifically employing Hebbian learning, for unsupervised topic modeling.
To introduce a novel Spiking Topic Model (STM) for text analysis.
To evaluate STM's performance against established TM algorithms.

Main Methods:

Text data is converted into spike sequences for SNN input.
A single-layer SNN is trained using spike-timing-dependent plasticity.
Each SNN neuron represents a distinct topic, with weights signifying word relevance.
STM performance is benchmarked against Latent Dirichlet Allocation (LDA), Biterm Topic Model (BTM), Embedding Topic Model (ETM), and BERTopic.

Main Results:

The proposed Spiking Topic Model (STM) successfully discovers high-quality topics.
STM demonstrates competitive performance compared to classical TM methods across three diverse datasets (20Newsgroups, BBC news, AG news).
The study validates the hypothesis that Hebbian-learning SNNs can specialize in detecting significant word patterns.

Conclusions:

SNNs show significant potential for application in unsupervised natural language processing tasks like topic modeling.
STM offers a novel, biologically plausible approach to uncovering thematic structures in text.
This research opens new avenues for exploring SNNs in computational linguistics and information retrieval.