Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA.
Proc Natl Acad Sci U S A. 2010 Oct 19;107(42):18149-54. doi: 10.1073/pnas.0914916107. Epub 2010 Oct 5.
The visual system is challenged with extracting and representing behaviorally relevant information contained in natural inputs of great complexity and detail. This task begins in the sensory periphery: retinal receptive fields and circuits are matched to the first and second-order statistical structure of natural inputs. This matching enables the retina to remove stimulus components that are predictable (and therefore uninformative), and primarily transmit what is unpredictable (and therefore informative). Here we show that this design principle applies to more complex aspects of natural scenes, and to central visual processing. We do this by classifying high-order statistics of natural scenes according to whether they are uninformative vs. informative. We find that the uninformative ones are perceptually nonsalient, while the informative ones are highly salient, and correspond to previously identified perceptual mechanisms whose neural basis is likely central. Our results suggest that the principle of efficient coding not only accounts for filtering operations in the sensory periphery, but also shapes subsequent stages of sensory processing that are sensitive to high-order image statistics.
视觉系统面临着从复杂而详细的自然输入中提取和表示具有行为相关性的信息的挑战。这个任务始于感觉外围：视网膜的感受野和回路与自然输入的一阶和二阶统计结构相匹配。这种匹配使视网膜能够去除可预测的（因此无信息）刺激成分，主要传递不可预测的（因此有信息）刺激成分。在这里，我们表明这个设计原则适用于自然场景更复杂的方面，以及中央视觉处理。我们通过根据自然场景的高阶统计数据是否无信息与有信息来对其进行分类。我们发现，无信息的那些在感知上不显著，而有信息的那些则非常显著，并且与先前确定的感知机制相对应，这些感知机制的神经基础可能是中央的。我们的结果表明，有效编码的原则不仅解释了感觉外围的滤波操作，而且还塑造了对高阶图像统计敏感的后续感觉处理阶段。
