Contour Curvature As an Invariant Code for Objects in Visual Area V4
Contour Curvature As an Invariant Code for Objects in Visual Area V4
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DOI：
10.1523/jneurosci.4139-15.2016
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发表时间：
2016-05-18
影响因子：
5.3
通讯作者：
Pasupathy, Anitha
中科院分区：
文献类型：
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作者：
El-Shamayleh, Yasmine;Pasupathy, Anitha
尺寸不变的物体识别——跨越尺度变换识别物体的能力——是生物和人工视觉的基本特征。为了研究其在灵长类动物大脑皮层中的基础，我们测量了恒河猴（Macaca mulatta）视觉区域V4中单个神经元对不同大小刺激的反应，V4是物体处理通路的基石。利用两个相互竞争的模型来解释神经元对物体边界轮廓的选择性如何取决于刺激大小，我们发现大多数V4神经元（接近70%）以大小不变的方式编码物体，与边界形式的大小无关参数的选择性一致：对于这些神经元，“标准化”曲率，而不是“绝对”曲率，提供了更好的响应说明。我们的研究结果证明了轮廓曲率作为视觉皮层中大小不变对象表示基础的适用性，并假设V4是行为相关对象代码的基础。
Size-invariant object recognition-the ability to recognize objects across transformations of scale-is a fundamental feature of biological and artificial vision. To investigate its basis in the primate cerebral cortex, we measured single neuron responses to stimuli of varying size in visual area V4, a cornerstone of the object-processing pathway, in rhesus monkeys (Macaca mulatta). Leveraging two competing models for how neuronal selectivity for the bounding contours of objects may depend on stimulus size, we show that most V4 neurons (similar to 70%) encode objects in a size-invariant manner, consistent with selectivity for a size-independent parameter of boundary form: for these neurons, "normalized" curvature, rather than "absolute" curvature, provided a better account of responses. Our results demonstrate the suitability of contour curvature as a basis for size-invariant object representation in the visual cortex, and posit V4 as a foundation for behaviorally relevant object codes.