CRCNS: Representation and Computation in Natural Vision

自然视觉中的表示和计算在自然环境中，物体在各种各样的照明条件、姿势、背景和与其他物体并置的情况下被观察。 足够不变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。 真实的图像的丰富结构提供了大量的机会安排，其中许多导致系统错误地检测到不存在的对象。另一方面，高选择性的系统同时也很容易在自然变化面前错过检测。相比之下，人类和动物的视觉系统能够在各种观察条件下准确地看到-生物系统是如何选择和不变的？对这个问题的追求导致了一个类似的问题，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。它们的优势似乎也是它们的弱点：视觉系统如何从不变性中建立选择性？ 复杂细胞模型提供了一种解释。复杂的和其他不变的细胞类型，凭借其非线性响应特性，必然具有功能连接性，由此这些细胞在功能上连接到其输入的通常小的子集。这种承诺是依情况而定的，因为它取决于感受野的特定模式。 功能连通性是一个可证明的数学性质，几乎所有的非线性模型提出了复杂的细胞感受场的属性。更重要的是，这些观察得出的结论是，具有重叠感受野的成对此类细胞将表现出功能性的共同输入。 这也是间接的，事实上，当各自感受野中的模式“匹配在一起”时，功能性共同输入就很高--对应于一个更大的整体的片段。 这些观察结果为不变性与选择性之间的困境提供了一个解决方案：正确组合在一起的片段会产生高度的功能性共同输入，这表现为在其他不变表征之间的统计依赖性，最有可能以部分同步的形式出现，从而向视觉通路中更深处的细胞发出组成部分的信号。 在寻找实验证实这个建议的答案的选择性/不变性的困境，研究人员采用新的统计和方法学技术来研究新的问题不变的细胞的感受野特性，并测量新的变量的联合统计不变的细胞重叠的感受野。

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.