Knowledge Commons of Institute of Automation,CAS
Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks | |
Shen, Guobin1,4; Zhao, Dongcheng1![]() ![]() | |
发表期刊 | Patterns
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2022 | |
页码 | 100522 |
摘要 | The spiking neural network (SNN) mimics the information-processing operation in the human brain. Directly applying backpropagation to the training of the SNN still has a performance gap compared with traditional deep neural networks. To address the problem, we propose a biologically plausible spatial adjustment that rethinks the relationship between membrane potential and spikes and realizes a reasonable adjustment of gradients to different time steps. It precisely controls the backpropagation of the error along the spatial dimension. Secondly, we propose a biologically plausible temporal adjustment to make the error propagate across the spikes in the temporal dimension, which overcomes the problem of the temporal dependency within a single spike period of traditional spiking neurons. We have verified our algorithm on several datasets, and the experimental results have shown that our algorithm greatly reduces network latency and energy consumption while also improving network performance. |
学科门类 | 工学::控制科学与工程 |
DOI | https://doi.org/10.1016/j.patter.2022.100522 |
URL | 查看原文 |
收录类别 | ESCI |
语种 | 英语 |
七大方向——子方向分类 | 类脑模型与计算 |
国重实验室规划方向分类 | 认知机理与类脑学习 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57255 |
专题 | 脑图谱与类脑智能实验室_类脑认知计算 |
通讯作者 | Zeng, Yi |
作者单位 | 1.Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences 2.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences 3.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 4.School of Future Technology, University of Chinese Academy of Sciences 5.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Shen, Guobin,Zhao, Dongcheng,Zeng, Yi. Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks[J]. Patterns,2022:100522. |
APA | Shen, Guobin,Zhao, Dongcheng,&Zeng, Yi.(2022).Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks.Patterns,100522. |
MLA | Shen, Guobin,et al."Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks".Patterns (2022):100522. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Patterns_BioLIF.pdf(1369KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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