宁夏大学学报（自然科学版中英文）2026，Vol.47Issue(1)：24-32,9.DOI:10.20176/j.cnki.nxdz.000096
一种生物视觉启发的高鲁棒性脉冲循环神经网络模型
A Highly Robust Spiking Recurrent Neural Network Model Inspired by Biological Vision
摘要
Abstract
To address the low robustness of Spiking Neural Networks(SNN)against adversarial attacks,a highly robust Spiking Recurrent Neural Network model inspired by biological vision was proposed.This model incorporates the biological mechanisms of the primary visual cortex(V1)and features a convolutional SNN front end designed with biological constraints.Additionally,by integrating feedback connections from the corti-cal visual information,an SNN back end with an internal recurrent mechanism was constructed.In the absence of adversarial training,this model demonstrates significant improvements in adversarial accuracy of 31.6%,22.11%,and 20.99%on the SVHN,CIFAR10,and CIFAR100 datasets,respectively.With adversarial training,the adversarial accuracy improves by 20.64%,8.79%,and 6.89%,respectively.Furthermore,as the perturbation factor(ε)and the time window(T)increase,the accuracy of this model consistently surpasses that of the baseline model.Experimental results show that the Spiking Recurrent Neural Network model,which incorporates biological vision mechanisms,shows significantly improved accuracy when faced with adver-sarial attacks,demonstrating enhanced adversarial robustness.关键词
脉冲神经网络/鲁棒性/对抗攻击/生物视觉Key words
spiking neural networks/robustness/adversarial attacks/biological vision分类
信息技术与安全科学引用本文复制引用
陈林果,黄荣,韩芳..一种生物视觉启发的高鲁棒性脉冲循环神经网络模型[J].宁夏大学学报（自然科学版中英文）,2026,47(1):24-32,9.基金项目
国家自然科学基金资助项目(12272092) （12272092）
