Publication: Ensembling SNNs with STDP Learning on Base of Rate Stabilization for Image Classification
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2021
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© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.In spite of a number of existing spiking neural network models for image classification, it still remains relevant from both methodological and practical points of view to develop a model as simple as possible, while at the same time applicable to classification tasks with various types of data, be it real vectors or images. Our previous work proposed a simple spiking network with Spike-Timing-Dependent-Plasticity (STDP) learning for solving real-vector classification tasks. In this paper, that method is extended to image recognition tasks and enhanced by aggregating neurons into ensembles. The network comprises one layer of neurons with STDP-plastic inputs receiving pixels of input images encoded with spiking rates. This work considers two approaches for aggregating neurons’ output activities within an ensemble: by averaging their output spiking rates (i.e. averaging outputs before decoding spiking rates into class labels) and by voting with decoded class labels. Ensembles aggregated by output frequencies are shown to achieve a significant accuracy increase up to 95% (by F1-score) for the Optdigits handwritten digit dataset, and is comparable with conventional machine learning approaches.
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Ensembling SNNs with STDP Learning on Base of Rate Stabilization for Image Classification / Serenko, A. [et al.] // Advances in Intelligent Systems and Computing. - 2021. - 1310. - P. 446-452. - 10.1007/978-3-030-65596-9_53