Knowledge Commons of Institute of Automation,CAS
LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition | |
Xiang Cheng1,2,3; Yunzhe Hao1,2,3; Jiaming Xu1,2; Bo Xu1,2,3,4 | |
2021-01 | |
会议名称 | the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) |
会议日期 | January 7-15, 2021 |
会议地点 | Yokohama, Japan |
出版者 | IJCAI-20 |
摘要 | Spiking Neural Network (SNN) is considered more biologically plausible and energy-efficient on emerging neuromorphic hardware. Recently back-propagation algorithm has been utilized for training SNN, which allows SNN to go deeper and achieve higher performance. However, most existing SNN models for object recognition are mainly convolutional structures or fully-connected structures, which only have inter-layer connections, but no intra-layer connections. Inspired by Lateral Interactions in neuroscience, we propose a high-performance and noise-robust Spiking Neural Network (dubbed LISNN). Based on the convolutional SNN, we model the lateral interactions between spatially adjacent neurons and integrate it into the spiking neuron membrane potential formula, then build a multi-layer SNN on a popular deep learning framework, i. e., PyTorch. We utilize the pseudo-derivative method to solve the non-differentiable problem when applying backpropagation to train LISNN and test LISNN on multiple standard datasets. Experimental results demonstrate that the proposed model can achieve competitive or better performance compared to current state-of-the-art spiking neural networks on MNIST, Fashion-MNIST, and N-MNIST datasets. Besides, thanks to lateral interactions, our model processes stronger noise-robustness than other SNN. Our work brings a biologically plausible mechanism into SNN, hoping that it can help us understand the visual information processing in the brain. |
收录类别 | EI |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48875 |
专题 | 复杂系统认知与决策实验室_听觉模型与认知计算 |
通讯作者 | Jiaming Xu; Bo Xu |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences (CASIA). Beijing, China 2.Research Center for Brain-inspired Intelligence, CASIA 3.University of Chinese Academy of Sciences 4.Center for Excellence in Brain Science and Intelligence Technology, CAS. China |
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 |
推荐引用方式 GB/T 7714 | Xiang Cheng,Yunzhe Hao,Jiaming Xu,et al. LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition[C]:IJCAI-20,2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
0211.pdf(2275KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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