CRCNS: Representation and Computation in Natural Vision

自然视觉中的表示和计算在自然环境中，可以在各种光照条件、姿势、背景以及与其他对象的并置情况下查看对象。具有足够不变性以适应这种变化的人工视觉系统永远不会有足够的选择性。真实图像的丰富结构提供了大量的机会安排，其中许多安排导致系统错误地检测到不存在的对象。另一方面，在面对自然变化时，高度选择性的系统同时也很容易遗漏检测。相比之下，人类和动物的视觉系统能够在广泛的观察条件下准确地看到东西--生物系统为什么既是选择性的，又是不变的？这个问题的追求引出了一个类似的问题，即复杂细胞和其他在腹侧视觉通路中普遍存在的不变细胞类型。他们的优势似乎就是他们的弱点：视觉系统如何可能从不变性中建立选择性？复杂细胞的模型给出了一个解释。复数和其他不变细胞类型凭借其非线性响应特性必然具有功能连通性，由此这些细胞变得在功能上连接到其输入的一般较小的子集。这种承诺是间接的，因为它取决于接受场中的特定模式。功能连通性是迄今为止提出的复杂细胞感受野特性的几乎所有非线性模型的一个明显的数学特性。更重要的是，这些观察得出的结论是，拥有重叠感受野的这类细胞对将展示出功能上的共同输入。这也是间接的，事实上，当各个接受域中的模式“结合在一起”-对应于一个更大整体的片段时，功能共同输入是高的。这些观察结果提出了一种解决不变性和选择性的两难困境的方法：适当组合在一起的片段会产生高度的功能共同输入，这表现为其他不同表征之间的统计依赖，很可能是部分同步的形式，从而向视觉通路中更深层次的细胞发出部分组成的信号。为了寻求对选择性/不变性困境提出的答案的实验证实，研究人员使用新的统计和方法技术来研究关于不变细胞感受野特性的新问题，并测量不变细胞与重叠感受野的联合统计中的新变量。

Representation and Computation in Natural VisionIn natural environments, objects are viewed under a wide variety of lighting conditions, poses, backgrounds, and juxtapositions with other objects. Artificial vision systems that are sufficiently invariant to accommodate such variations are never sufficiently selective. The rich structure of real images offers a multitude of chance arrangements, many of which cause systems to falsely detect an object that is not there. On the other hand, systems that are highly selective are at the same time highly prone to missed detections in the face of natural variability. The visual systems of humans and animals, in contrast, are able to see accurately under a wide range of viewing conditions--how is it that biological systems are both selective and invariant?The pursuit of this question leads to an analogous question about complex cells and other invariant cell types that are ubiquitous in the ventral visual pathway. Their strength would appear to be their weakness: How is it possible for the visual system to build selectivity out of invariance? Models of complex cells suggest an explanation. Complex and other invariant cell types, by virtue of their nonlinear response characteristics, necessarily possess a functional connectivity whereby these cells become functionally connected to a generally small subset of their inputs. This commitment is circumstantial, inasmuch as it depends on the particular pattern in the receptive field. Functional connectivity is a demonstrable mathematical property of virtually all of the non-linear models put forward to date for complex-cell receptive-field properties. What is more, these observations lead to the conclusion that pairs of such cells that possess overlapping receptive fields will demonstrate a functional common input. This too is circumstantial, and in fact functional common input is high exactly when the patterns in the respective receptive fields "fit together"---correspond to pieces of a larger whole. These observations suggest a solution to the dilemma of invariance versus selectivity: pieces that fit properly together generate a high degree of functional common input, which manifests itself by a statistical dependence between otherwise invariant representations, most likely in the form of partial synchrony, thereby signaling a composition of parts to cells deeper in the visual pathway. In search of experimental confirmation of this proposed answer to the selectivity/invariance dilemma, the investigators employ new statistical and methodological techniques to study new questions about the receptive-field properties of invariant cells, and to measure new variables in the joint statistics of invariant cells with overlapping receptive fields.