现代信息科技2024，Vol.8Issue(10)：1-6,6.DOI:10.19850/j.cnki.2096-4706.2024.10.001
模拟神经反馈机制和工作记忆的图像分类网络模型
Image Classification Network Model of Simulated Neural Feedback Mechanism and Working Memory
摘要
Abstract
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.关键词
反馈机制/工作记忆/循环计算/图像分类Key words
feedback mechanism/working memory/loop computation/image classification分类
信息技术与安全科学引用本文复制引用
童顺延,刘海华..模拟神经反馈机制和工作记忆的图像分类网络模型[J].现代信息科技,2024,8(10):1-6,6.基金项目
湖北省自然科学基金资助项目(61773409) （61773409）
