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Alternative TitleSelf-adaptive coding for spiking neural network
张驰1,2,3; 唐凤珍1,2
Source Publication计算机应用研究
Contribution Rank1
Funding Organization国家重点研发计划资助项目(2020YFB13400) ; 国家自然科学基金资助项目(61803369)
Keyword脉冲神经网络 自适应编码 替代梯度反向传播 LIF神经元模型


Other Abstract

Spiking neural networks (SNN) , using spikes to represent and convey information, are more biologically plausible than traditional artificial neural networks. However, a classical shallow SNN has limited feature exraction ability due to the shallow network structure, leading to inferior classification performance to convolutional neural networks (CNN) especially on multi-class classification tasks such as object categorization. In this paper, inspired by the powerful convolutional structure of CNN, a self-adaptive coding spiking neural network (SCSNN) is proposed. By exploiting convolutional structures and dynamic impulse triggerred property of biological neurons, the proposed SCSNN organizes integrate-and-firing models in a convolutional fashion, and trained by a new surrogate gradient back-propagation algorithm directly. The proposed SCSNN network was validated on the MNIST dataset and the Fashion-MNIST dataset, respectively, obtaining superior performance to state-or-the-art SNN networks on both datasets. The classification accuracy on the MNIST dataset reaches 99. 62%, comparable to the performance of exsiting SNN networks. The classification accuracy on the Fashion-MNIST dataset reaches 93. 52%, significantly better than exsiting SNN networks, confirming the effectiveness of the proposed model.

Document Type期刊论文
Corresponding Author唐凤珍
Recommended Citation
GB/T 7714
张驰,唐凤珍. 基于自适应编码的脉冲神经网络[J]. 计算机应用研究,2021:1-6.
APA 张驰,&唐凤珍.(2021).基于自适应编码的脉冲神经网络.计算机应用研究,1-6.
MLA 张驰,et al."基于自适应编码的脉冲神经网络".计算机应用研究 (2021):1-6.
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