一种生物视觉启发的高鲁棒性脉冲循环神经网络模型OA

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.