Pattern Recognition Spiking Neural Network for Classification of Chinese Characters

Russo, Nicola, Yuzhong, Wang, Madsen, Thomas ORCID: and Nikolic, Konstantin ORCID: (2023) Pattern Recognition Spiking Neural Network for Classification of Chinese Characters. In: ESANN 2023 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 4-6 Oct 2023, Bruges (Belgium).

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The Spiking Neural Networks (SNNs) are biologically more
realistic than other types of Artificial Neural Networks (ANNs), but they have been much less utilised in applications. When comparing the two types of NNs, the SNNs are considered to be of lower latency, more
hardware-friendly and energy-efficient, and suitable for running on portable devices with weak computing performance. In this paper we aim to use an SNN for the task of classifying Chinese character images, and test its
performance. The network utilises inhibitory synapses for the purpose of using unsupervised learning. The learning algorithm is a derivative of the traditional Spike-timing-dependent Plasticity (STDP) learning rule. The input images are first pre-processed by traditional methods (OpenCV).Different hyperparameters configurations are tested reaching an optimal configuration and a classification accuracy rate of 93%.

Item Type: Conference or Workshop Item (Paper)
ISBN: 978-2-87587-088-9
Page Range: pp. 1-6
Keywords: Spiking Neural Networks (SNNs), Chinese characters
Subjects: Computing
Depositing User: Konstantin Nikolic
Date Deposited: 16 Oct 2023 14:16
Last Modified: 16 Oct 2023 14:16


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