摘要
在我们以前提出的注意力选择模型上,提出一个带有学习和遗忘的视觉记忆模型:遗忘增量多层分类回归树,来模拟人脑的长短期记忆.同时自监督竞争神经网络综合自下而上和自上而下的信息找到注意力的焦点,该网络各个神经元的连接权根据环境变化在线调整,从而实现整个网络的在线学习.实验证明,该模型能够模拟人的注意力转移,并能在变化的环境中,有意识地盯住感兴趣的物体.
Based on the previous attention selection model with visual memory as top-down guidance, a visual memory model is put forward with online learning and forgetting, called amnesic incremental hierarchical discriminant regression （AIHDR） tree, to mimic human short-term memory （STM） and long-term memory （LTM）. A self-supervised competition neural network （SSCNN） combines the information from both bottom-up and top-down to find out the focus of attention （FoA）. The connection weights in SSCNN can be updated in real-time according to the environment. Experimental results show that the proposed model can mimic the shift of human attention and stare at an interesting object consciously when environment changes.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第3期381-387,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金资助项目(No.60571052
60671062)
