Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California 92037, USA.
Department of Physics, University of California San Diego, La Jolla, California 92093, USA.
Nat Commun. 2017 Jun 8;8:15739. doi: 10.1038/ncomms15739.
Object recognition relies on a series of transformations among which only the first cortical stage is relatively well understood. Already at the second stage, the visual area V2, the complexity of the transformation precludes a clear understanding of what specifically this area computes. Previous work has found multiple types of V2 neurons, with neurons of each type selective for multi-edge features. Here we analyse responses of V2 neurons to natural stimuli and find three organizing principles. First, the relevant edges for V2 neurons can be grouped into quadrature pairs, indicating invariance to local translation. Second, the excitatory edges have nearby suppressive edges with orthogonal orientations. Third, the resulting multi-edge patterns are repeated in space to form textures or texture boundaries. The cross-orientation suppression increases the sparseness of responses to natural images based on these complex forms of feature selectivity while allowing for multiple scales of position invariance.
目标识别依赖于一系列变换，其中只有第一个皮层阶段相对较好理解。在第二个阶段，即视觉区域 V2，变换的复杂性使得难以清楚地理解该区域具体计算了什么。先前的工作已经发现了多种类型的 V2 神经元，每种类型的神经元都对多边缘特征具有选择性。在这里，我们分析了 V2 神经元对自然刺激的反应，发现了三个组织原则。首先，V2 神经元的相关边缘可以分为正交对，表明对局部平移的不变性。其次，兴奋边缘有附近具有正交方向的抑制边缘。第三，形成纹理或纹理边界的多边缘模式在空间中重复。基于这种复杂的特征选择性，交叉方向抑制增加了对自然图像的响应稀疏性，同时允许多个位置不变尺度。
