Shape representation in area V4: Position-specific tuning for boundary conformation
Shape representation in area V4: Position-specific tuning for boundary conformation
复制标题
DOI：
10.1152/jn.2001.86.5.2505
复制
发表时间：
2001-11-01
影响因子：
2.5
通讯作者：
Connor, CE
中科院分区：
文献类型：
--
作者：
Pasupathy, A;Connor, CE
灵长类动物的视觉形状识别依赖于一条从初级视觉皮质(V1)到颞下皮质(IT)的多阶段通路。在信息技术中，来自V1的局部形状信号被转换为对抽象对象类别的选择性的机制尚不清楚。解决这一问题的一种方法是研究通路中间阶段的形状表征，如V4区。我们在初步试验中研究了109个对复杂形状似乎敏感的V4细胞。为了在V4中实现更完整的形状表示，我们用一组366个刺激测试了每个单元，这些刺激是通过系统地将凸和凹边界元素组合成闭合形状来构建的。使用这个庞大的、多样化的刺激集，我们发现我们样本中的所有细胞对各种各样的形状都有反应，似乎没有编码任何单一类型的全局形状。然而，对于大多数细胞来说，引起最强烈反应的形状的特征是在刺激内的特定位置具有一致类型的边界构象。例如，给定的单元格可以针对右侧包含凹曲率的形状进行调整，而形状的其他部分对响应影响很小或没有影响。许多单元被调整为更复杂的边界配置(例如，与凹曲线相邻的凸角)。我们在曲率x位置域上用高斯函数量化了这种形状调谐。这些调谐函数比基于边缘或轴方向的调谐函数更适合神经反应。因此，单个V4单元似乎在较大形状内的特定位置编码中等复杂的边界信息。这一发现表明，在V1-IT转换的中间阶段，复杂对象至少部分地根据其轮廓分量的配置和位置来表征。
Visual shape recognition in primates depends on a multi-stage pathway running from primary visual cortex (V1) to inferotemporal cortex (IT). The mechanisms by which local shape signals from V1 are transformed into selectivity for abstract object categories in IT are unknown. One approach to this issue is to investigate shape representation at intermediate stages in the pathway, such as area V4. We studied 109 V4 cells that appeared sensitive to complex shape in preliminary tests. To achieve a more complete picture of shape representation in V4, we tested each cell with a set of 366 stimuli, constructed by systematically combining convex and concave boundary elements into closed shapes. Using this large, diverse stimulus set, we found that all the cells in our sample responded to a wide variety of shapes and did not appear to encode any single type of global shape. However, for most cells the shapes evoking strongest responses were characterized by a consistent type of boundary conformation at a specific position within the stimulus. For example, a given cell might be tuned for shapes containing concave curvature at the right, with other parts of the shape having little or no effect on responses. Many cells were tuned for more complex boundary configurations (e.g., a convex angle adjacent to a concave curve). We quantified this kind of shape tuning with Gaussian functions on a curvature x position domain. These tuning functions fit the neural responses much better than tuning functions based on edge or axis orientation. Thus individual V4 cells appear to encode moderately complex boundary information at specific locations within larger shapes. This finding suggests that, at intermediate stages in the V1-IT transformation, complex objects are represented at least partly in terms of the configurations and positions of their contour components.