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Cascade Spiking Neuron Network For Event-based Image Classification In Noisy Environment

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posted on 10.09.2021, 20:15 by Yuntao HanYuntao Han, Tao Yu, Silu Cheng, Jiangtao Xu

Spiking Neuron Network (SNN) has shown advantages in processing event-based data for image classification. However, the classification accuracy of SNNs decreases in noisy environment. The cascade spiking neuron network (cascade-SNN) was proposed to solve this problem in this letter. We used spiking convolutional spiking neuron network (SCNN) for features extraction and liquid state machine (LSM) for read out. Compared with early works on ANNs, this network achieved the state-of-the-art classification accuracy in DVS-CIFAR10 dataset and DVS-Gesture dataset, which are both challenging dataset because of noisy environment. We conducted ablation experiments to verify the proposed structure is effective and analyzed the influence of different hyper-parameters.

Funding

National Natural Science Foundation of China under Grant 61774110

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Email Address of Submitting Author

hyt_@tju.edu.cn

ORCID of Submitting Author

0000-0003-0054-6479

Submitting Author's Institution

Tianjin University

Submitting Author's Country

China

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