Hosoya Haruo, Hyvärinen Aapo
Cognitive Mechanisms Laboratories, ATR International, Kyoto, Japan.
Department of Computer Science and HIIT, University of Helsinki, Helsinki, Finland.
PLoS Comput Biol. 2017 Jul 25;13(7):e1005667. doi: 10.1371/journal.pcbi.1005667. eCollection 2017 Jul.
Experimental studies have revealed evidence of both parts-based and holistic representations of objects and faces in the primate visual system. However, it is still a mystery how such seemingly contradictory types of processing can coexist within a single system. Here, we propose a novel theory called mixture of sparse coding models, inspired by the formation of category-specific subregions in the inferotemporal (IT) cortex. We developed a hierarchical network that constructed a mixture of two sparse coding submodels on top of a simple Gabor analysis. The submodels were each trained with face or non-face object images, which resulted in separate representations of facial parts and object parts. Importantly, evoked neural activities were modeled by Bayesian inference, which had a top-down explaining-away effect that enabled recognition of an individual part to depend strongly on the category of the whole input. We show that this explaining-away effect was indeed crucial for the units in the face submodel to exhibit significant selectivity to face images over object images in a similar way to actual face-selective neurons in the macaque IT cortex. Furthermore, the model explained, qualitatively and quantitatively, several tuning properties to facial features found in the middle patch of face processing in IT as documented by Freiwald, Tsao, and Livingstone (2009). These included, in particular, tuning to only a small number of facial features that were often related to geometrically large parts like face outline and hair, preference and anti-preference of extreme facial features (e.g., very large/small inter-eye distance), and reduction of the gain of feature tuning for partial face stimuli compared to whole face stimuli. Thus, we hypothesize that the coding principle of facial features in the middle patch of face processing in the macaque IT cortex may be closely related to mixture of sparse coding models.
实验研究揭示了灵长类视觉系统中物体和面孔基于部分和整体表征的证据。然而，这样看似矛盾的处理类型如何能在单个系统中共存仍是个谜。在此，我们提出一种名为稀疏编码模型混合的新理论，其灵感来源于颞下（IT）皮质中类别特异性子区域的形成。我们开发了一个层次网络，该网络在简单的伽柏分析之上构建了两个稀疏编码子模型的混合。每个子模型分别用面部或非面部物体图像进行训练，从而产生面部部分和物体部分的单独表征。重要的是，诱发的神经活动通过贝叶斯推理进行建模，其具有自上而下的消除效应，使得对单个部分的识别强烈依赖于整个输入的类别。我们表明，这种消除效应对于面部子模型中的单元以类似于猕猴IT皮质中实际的面部选择性神经元的方式对面部图像而非物体图像表现出显著选择性确实至关重要。此外，该模型定性和定量地解释了Freiwald、Tsao和Livingstone（2009年）记录的IT中面部处理中间区域发现的几种对面部特征的调谐特性。这些特性尤其包括仅调谐到与诸如面部轮廓和头发等几何上较大部分相关的少数面部特征、对极端面部特征（例如，非常大/小的两眼间距）的偏好和反偏好，以及与全脸刺激相比部分面部刺激的特征调谐增益的降低。因此，我们假设猕猴IT皮质中面部处理中间区域面部特征的编码原则可能与稀疏编码模型的混合密切相关。
