Abstract
Spiking neural networks (SNNs) are potentially capable of greatly reducing the energy requirements of modern intelligent systems when combined with neuromorphic computing devices based on memristors, that facilitate on-chip SNN training. Currently, the existing spiking approaches either rely on weight transfer and/or backpropagation-based training or utilize large fully-connected spiking networks, imposing high hardware requirements. In this paper, we study the application of the bagging ensembling technique coupled with SNN-based models to the audio and image classification problems. In our experiments, we use a three-layer spiking neural network with Logistic Regression decoding and consider three local plasticity rules—spike time-dependent plasticity and its nanocomposite and poly-p-xylylene memristor counterparts. Using the Digits and FSDD datasets for training and evaluation, we show that bagging yields a performance increase of up to 20% in terms of the F1-score metric, while substantially reducing the total number of connections in the network.
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Acknowledgements
The study has been supported by the Russian Science Foundation grant No. 21-11-00328 https://rscf.ru/project/21-11-00328/ and has been carried out using computing resources of the federal collective usage center Complex for Simulation and Data Processing for Mega-science Facilities at NRC “Kurchatov Institute”, http://ckp.nrcki.ru/.
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Rybka, R., Davydov, Y., Sboev, A., Vlasov, D., Serenko, A. (2024). Image and Audio Data Classification Using Bagging Ensembles of Spiking Neural Networks with Memristive Plasticity. In: Samsonovich, A.V., Liu, T. (eds) Biologically Inspired Cognitive Architectures 2023. BICA 2023. Studies in Computational Intelligence, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-031-50381-8_80
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DOI: https://doi.org/10.1007/978-3-031-50381-8_80
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