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Spectral receptive fields do not explain tuning for boundary curvature in V4

机译:光谱接收场无法解释V4中边界曲率的调整

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摘要

The midlevel visual cortical area V4 in the primate is thought to be critical for the neural representation of visual shape. Several studies agree that V4 neurons respond to contour features, e.g., convexities and concavities along a shape boundary, that are more complex than the oriented segments encoded by neurons in the primary visual cortex. Here we compare two distinct approaches to modeling V4 shape selectivity: one based on a spectral receptive field (SRF) map in the orientation and spatial frequency domain and the other based on a map in an object-centered angular position and contour curvature space. We test the ability of these two characterizations to account for the responses of V4 neurons to a set of parametrically designed two-dimensional shapes recorded previously in the awake macaque. We report two lines of evidence suggesting that the SRF model does not capture the contour sensitivity of V4 neurons. First, the SRF model discards spatial phase information, which is inconsistent with the neuronal data. Second, the amount of variance explained by the SRF model was significantly less than that explained by the contour curvature model. Notably, cells best fit by the curvature model were poorly fit by the SRF model, the latter being appropriate for a subset of V4 neurons that appear to be orientation tuned. These limitations of the SRF model suggest that a full understanding of midlevel shape representation requires more complicated models that preserve phase information and perhaps deal with object segmentation.
机译:灵长类动物的中层视觉皮层区域V4被认为对于视觉形状的神经表示至关重要。几项研究认为,V4神经元对轮廓特征有反应,例如沿着形状边界的凸凹,这些特征比初级视觉皮层中神经元编码的定向节段复杂得多。在这里,我们比较了两种不同的V4形状选择性建模方法:一种基于方向和空间频域中的光谱接收场(SRF)映射,另一种基于对象中心角位置和轮廓曲率空间中的映射。我们测试这两个特征的能力,以解释V4神经元对先前在清醒的猕猴中记录的一组参数化设计的二维形状的响应。我们报告了两条证据,表明SRF模型不能捕获V4神经元的轮廓敏感性。首先,SRF模型丢弃空间相位信息,该信息与神经元数据不一致。其次,由SRF模型解释的方差量显着小于轮廓曲率模型解释的方差量。值得注意的是,最适合于曲率模型的细胞不适用于SRF模型,后者适合于似乎已调整方向的V4神经元子集。 SRF模型的这些局限性表明,对中层形状表示的全面了解需要更复杂的模型,这些模型可以保存相位信息并可能处理对象分割。

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