Okazawa Gouki, Tajima Satohiro, Komatsu Hidehiko
Division of Sensory and Cognitive Information, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan;
RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Japan Society for the Promotion of Science, Chiyoda, Tokyo 102-0082, Japan; and.
Proc Natl Acad Sci U S A. 2015 Jan 27;112(4):E351-60. doi: 10.1073/pnas.1415146112. Epub 2014 Dec 22.
Our daily visual experiences are inevitably linked to recognizing the rich variety of textures. However, how the brain encodes and differentiates a plethora of natural textures remains poorly understood. Here, we show that many neurons in macaque V4 selectively encode sparse combinations of higher-order image statistics to represent natural textures. We systematically explored neural selectivity in a high-dimensional texture space by combining texture synthesis and efficient-sampling techniques. This yielded parameterized models for individual texture-selective neurons. The models provided parsimonious but powerful predictors for each neuron's preferred textures using a sparse combination of image statistics. As a whole population, the neuronal tuning was distributed in a way suitable for categorizing textures and quantitatively predicts human ability to discriminate textures. Together, we suggest that the collective representation of visual image statistics in V4 plays a key role in organizing the natural texture perception.
我们日常的视觉体验不可避免地与识别丰富多样的纹理相关联。然而，大脑如何编码和区分大量的自然纹理仍知之甚少。在这里，我们表明猕猴V4区的许多神经元选择性地编码高阶图像统计量的稀疏组合来表征自然纹理。我们通过结合纹理合成和高效采样技术，在高维纹理空间中系统地探索了神经选择性。这产生了单个纹理选择性神经元的参数化模型。这些模型使用图像统计量的稀疏组合，为每个神经元的偏好纹理提供了简洁而强大的预测器。作为一个整体群体，神经元调谐以适合纹理分类的方式分布，并定量预测人类区分纹理的能力。我们共同认为，V4区视觉图像统计量的集体表征在组织自然纹理感知中起着关键作用。
