Spiking neural networks on tabular data for time series classification

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master_Chatiras_Marios_2025.pdf (944.05 KB)

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School of Electrical Engineering | Master's thesis
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Date

2025-07-25

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Major/Subject

Autonomous Systems

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Degree programme

Master's Programme in ICT Innovation

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en

Pages

86

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Abstract

Spiking Neural Networks (SNNs) have a biologically inspired neuron function and have demonstrated more energy efficiency than Artificial Neural Networks (ANNs) when implemented on specialized hardware. One of the challenges in using SNNs is encoding input data into the discrete spike-based representation that SNNs neurons use. This work investigates the use of SNNs with and without input rate encoding in time series classification tasks and compares their performance against conventional ANNs and non-neural network Machine Learning techniques. The study shows that the effectiveness of ANNs and SNNs architectures is strongly dependent on the dataset used, while a Decision Tree method employing Gradient Boosting showed robust performance across the experiments. While the SNNs architecture without rate encoding showed resilience with limited data availability and surpassed the performance of the ANNs in all but one of our datasets, the SNN architecture with rate encoding showed potential in specific scenarios but underperformed in achieving universal effectiveness. Cosine similarity analysis revealed a limitation of rate encoding, with small or negative numerical values encoded as spike trains containing zeros, constraining the model’s ability to recognize similarities and differences between instances belonging to the same and different classes. We also performed experiments with increased time steps in the SNNs architectures that revealed minimal enhancements in performance, suggesting the model’s effectiveness is subject to other parameters.

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Supervisor

Zhou, Quan

Thesis advisor

Dán, György

Keywords

signal encoding, spiking neural networks, time series classification, machine learning, artificial neural networks, data encoding

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https://urn.fi/URN:NBN:fi:aalto-202508196473

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[dipl] Sähkötekniikan korkeakoulu / ELEC