Klam F, Zemel R S, Pouget A
Vision Center Laboratory, Salk Institute, La Jolla, CA 92037, USA.
Neural Comput. 2008 Jan;20(1):146-75. doi: 10.1162/neco.2008.20.1.146.
The codes obtained from the responses of large populations of neurons are known as population codes. Several studies have shown that the amount of information conveyed by such codes, and the format of this information, is highly dependent on the pattern of correlations. However, very little is known about the impact of response correlations (as found in actual cortical circuits) on neural coding. To address this problem, we investigated the properties of population codes obtained from motion energy filters, which provide one of the best models for motion selectivity in early visual areas. It is therefore likely that the correlations that arise among energy filters also arise among motion-selective neurons. We adopted an ideal observer approach to analyze filter responses to three sets of images: noisy sine gratings, random dots kinematograms, and images of natural scenes. We report that in our model, the structure of the population code varies with the type of image. We also show that for all sets of images, correlations convey a large fraction of the information: 40% to 90% of the total information. Moreover, ignoring those correlations when decoding leads to considerable information loss-from 50% to 93%, depending on the image type. Finally we show that it is important to consider a large population of motion energy filters in order to see the impact of correlations. Study of pairs of neurons, as is often done experimentally, can underestimate the effect of correlations.
从大量神经元的反应中获得的编码被称为群体编码。多项研究表明，此类编码所传达的信息量以及该信息的格式高度依赖于相关性模式。然而，关于反应相关性（如在实际皮层回路中发现的）对神经编码的影响，人们了解得非常少。为了解决这个问题，我们研究了从运动能量滤波器获得的群体编码的特性，运动能量滤波器为早期视觉区域的运动选择性提供了最佳模型之一。因此，能量滤波器之间出现的相关性很可能也出现在运动选择性神经元之间。我们采用理想观察者方法来分析滤波器对三组图像的反应：噪声正弦光栅、随机点运动图和自然场景图像。我们报告称，在我们的模型中，群体编码的结构随图像类型而变化。我们还表明，对于所有图像组，相关性传达了很大一部分信息：占总信息的40%至90%。此外，在解码时忽略这些相关性会导致相当大的信息损失——根据图像类型，损失从50%到93%不等。最后我们表明，考虑大量运动能量滤波器以观察相关性的影响很重要。像实验中经常做的那样研究成对的神经元，可能会低估相关性的影响。
