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Oh say can you see? The physiology of vision

机译:哦,说可以看到吗?视觉生理

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Abstract: How can we see? One answer lies in the receptive fields of visual cells in our eyes and brain. A 'receptive field' in the simplest terms is a map of the regions in space where light can affect a cell's electrical output. Millions of such fields analyze and filter the patterns of light that impinge on the retina. I will illustrate the major anatomical structures and physiological processes underlying such fields, in the primary visual pathway from the eye to the brain. An understanding of such fields is critically important, since their output provides the basis upon which conscious visual perception can eventually be constructed by higher brain processes. That is, perception itself is derived from the information as filtered and analyzed by such fields. Complete spatio-temporal receptive fields of simple cells in the visual cortex of monkeys were recently recorded using white-noise analysis techniques by Dan Pollen and colleagues. I discuss various models of the shapes of these fields (Gaussian derivative, Gabor, edge-and-line- detector, and difference-of-offset-Gaussian) in the context of these new data. The Gaussian derivative model provided the simplest and most concise description of the receptive fields to the models tested. Gaussian derivative machine vision spatio-temporal filters, based upon the biological data, produced robust estimates of the spatial and temporal derivatives of the image. These should prove suitable for form, motion, color, and stereo analysis, using only linear, separable filters or their linear combinations. So a partial answer to 'How can we see?' may be that receptive fields in the early visual system may serve as robust derivative analyzers in space and time.!
机译:摘要:我们怎么看?一个答案在于我们的眼睛和大脑中视觉细胞的接受区域。用最简单的术语来说,“感受野”是空间中光会影响细胞的电输出的区域的图。数以百万计的此类场分析并过滤了入射到视网膜上的光的模式。我将在从眼睛到大脑的主要视觉路径中说明这些领域的主要解剖结构和生理过程。对这些领域的理解至关重要,因为它们的输出提供了最终的意识可以通过更高的大脑过程来构建有意识的视觉的基础。也就是说,感知本身是从由此类字段过滤和分析的信息中得出的。 Dan Pollen及其同事最近使用白噪声分析技术记录了猴子视皮层中简单细胞的完整时空接受域。在这些新数据的背景下,我将讨论这些场的形状的各种模型(高斯导数,Gabor,边线检测器和偏移高斯差)。高斯导数模型为接受测试的模型提供了最简单,最简洁的接收场描述。基于生物数据的高斯导数机器视觉时空滤波器产生了图像的时空导数的鲁棒估计。仅使用线性,可分离的过滤器或它们的线性组合,这些应证明适用于形式,运动,颜色和立体分析。因此,对“我们怎么能看到?”的部分答案。可能是早期视觉系统中的感受野可以充当时空的鲁棒派生分析器。

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