基于cnn的人类早期视觉系统建模

Seung-Pyo Chae, Jeong-Woo Lee, M. Kim, Si-Yeol Kim, Jin-Ho Cho
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引用次数: 1

摘要

引入基于细胞神经网络(CNN)的视网膜模型来模拟人类早期视觉系统。通过使用CNN考虑视网膜活动,我们可以开始考虑整个视网膜在空间/时间上的相互作用,并考虑大量细胞作为边缘检测和移动刺激检测的机制。在模拟视网膜各神经元的输出响应后,计算出在角膜处记录的体积传导电位ERG,作为眼科临床重要的诊断指标。构成典型ERG的每一个小波都与视网膜特殊层的形成机制密切相关,通过分析各小波形式可以推测视网膜特殊层的临床状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNN-based modeling of the human early vision system
CNN (cellular neural network)-based retina model is introduced to simulate the human early vision system. By considering the retinal activity using CNN, we can begin to think about whole retinal interactions in space/time and consider the mechanism of a large population of cells as the edge detection and detection of moving stimuli. Furthermore, after simulating the output response of each retinal neuron, ERG, which is volume conductor potential recorded at the cornea and used as important diagnostic measure in eye clinic, is calculated. Each wavelet which composes the typical ERG has close relationship with the mechanism of a special retinal layer and by analyzing the each wavelet form we can guess the clinical state of special retinal layer.
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