Shin Joo-Heon, Smith David, Swiercz Waldemar, Staley Kevin, Rickard J Terry, Montero Javier, Kurgan Lukasz A, Cios Krzysztof J
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284 USA.
IEEE Trans Neural Netw. 2010 Nov;21(11):1697-709. doi: 10.1109/TNN.2010.2050600.
In this paper, we introduce a novel system for recognition of partially occluded and rotated images. The system is based on a hierarchical network of integrate-and-fire spiking neurons with random synaptic connections and a novel organization process. The network generates integrated output sequences that are used for image classification. The proposed network is shown to provide satisfactory predictive performance given that the number of the recognition neurons and synaptic connections are adjusted to the size of the input image. Comparison of synaptic plasticity activity rule (SAPR) and spike timing dependant plasticity rules, which are used to learn connections between the spiking neurons, indicates that the former gives better results and thus the SAPR rule is used. Test results show that the proposed network performs better than a recognition system based on support vector machines.
在本文中，我们介绍了一种用于识别部分遮挡和旋转图像的新型系统。该系统基于具有随机突触连接的积分发放脉冲神经元的分层网络以及一种新颖的组织过程。该网络生成用于图像分类的综合输出序列。结果表明，在将识别神经元和突触连接的数量调整到输入图像大小时，所提出的网络能提供令人满意的预测性能。比较用于学习脉冲神经元之间连接的突触可塑性活动规则（SAPR）和脉冲时间依赖可塑性规则，结果表明前者效果更好，因此采用了SAPR规则。测试结果表明，所提出的网络比基于支持向量机的识别系统表现更好。
