Image Classification Network Model of Simulated Neural Feedback Mechanism and Working Memory
This project introduces neural feedback and working memory mechanisms into Convolutional Neural Networks(CNN)and proposes an Intra-Layer Deep Feedback Convolutional Neural Network Model(IDFNet).The network constructs a Deep Feedback Structure(DFS)using neural feedback mechanisms,and introduces a Working Memory(WM)within this module.It controls the update of WM spatial content by depth variations,so as to enhance information retrieval capabilities.Finally,the IDFNet network is built by replacing CNN's convolutional layers with DFS.Experimental results on the Flower102,CIFAR-10,and CIFAR-100 datasets demonstrate that IDFNet achieves significant performance improvements compared to similar networks,with higher recognition rates of 96.61%,95.87%,and 79.99%,respectively,while requiring fewer parameters and computations.
国家科技期刊平台
NSTL
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