Gutmann Michael U, Laparra Valero, Hyvärinen Aapo, Malo Jesús
Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland ; Helsinki Institute for Information Technology, University of Helsinki, Helsinki, Finland.
Image Processing Laboratory, Universitat de València, València, Spain.
PLoS One. 2014 Feb 12;9(2):e86481. doi: 10.1371/journal.pone.0086481. eCollection 2014.
Independent component and canonical correlation analysis are two general-purpose statistical methods with wide applicability. In neuroscience, independent component analysis of chromatic natural images explains the spatio-chromatic structure of primary cortical receptive fields in terms of properties of the visual environment. Canonical correlation analysis explains similarly chromatic adaptation to different illuminations. But, as we show in this paper, neither of the two methods generalizes well to explain both spatio-chromatic processing and adaptation at the same time. We propose a statistical method which combines the desirable properties of independent component and canonical correlation analysis: It finds independent components in each data set which, across the two data sets, are related to each other via linear or higher-order correlations. The new method is as widely applicable as canonical correlation analysis, and also to more than two data sets. We call it higher-order canonical correlation analysis. When applied to chromatic natural images, we found that it provides a single (unified) statistical framework which accounts for both spatio-chromatic processing and adaptation. Filters with spatio-chromatic tuning properties as in the primary visual cortex emerged and corresponding-colors psychophysics was reproduced reasonably well. We used the new method to make a theory-driven testable prediction on how the neural response to colored patterns should change when the illumination changes. We predict shifts in the responses which are comparable to the shifts reported for chromatic contrast habituation.
独立成分分析和典型相关分析是两种具有广泛适用性的通用统计方法。在神经科学中，对彩色自然图像进行独立成分分析，可根据视觉环境的特性来解释初级皮层感受野的时空色度结构。典型相关分析同样能解释对不同光照的颜色适应。但是，正如我们在本文中所展示的，这两种方法都不能很好地同时推广用于解释时空色度处理和适应这两个方面。我们提出了一种结合了独立成分分析和典型相关分析优良特性的统计方法：它在每个数据集中找到独立成分，这些独立成分在两个数据集之间通过线性或高阶相关性相互关联。这种新方法与典型相关分析一样具有广泛的适用性，并且还适用于两个以上的数据集。我们将其称为高阶典型相关分析。当应用于彩色自然图像时，我们发现它提供了一个单一（统一）的统计框架，该框架既能解释时空色度处理又能解释适应情况。具有如初级视觉皮层中那样的时空色度调谐特性的滤波器出现了，并且相应颜色的心理物理学得到了较好的再现。我们使用这种新方法对光照变化时神经对彩色图案的反应应如何变化进行了理论驱动的可测试预测。我们预测反应的变化与报道的颜色对比度习惯化的变化相当。
