Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan.
ATR Computational Neuroscience Laboratories, Seika, Soraku, Kyoto 619-0288, Japan.
eNeuro. 2017 Apr 4;4(2). doi: 10.1523/ENEURO.0268-16.2017. eCollection 2017 Mar-Apr.
Neurons in high-level visual areas respond to more complex visual features with broader receptive fields (RFs) compared to those in low-level visual areas. Thus, high-level visual areas are generally considered to carry less information regarding the position of seen objects in the visual field. However, larger RFs may not imply loss of position information at the population level. Here, we evaluated how accurately the position of a seen object could be predicted (decoded) from activity patterns in each of six representative visual areas with different RF sizes [V1-V4, lateral occipital complex (LOC), and fusiform face area (FFA)]. We collected functional magnetic resonance imaging (fMRI) responses while human subjects viewed a ball randomly moving in a two-dimensional field. To estimate population RF sizes of individual fMRI voxels, RF models were fitted for individual voxels in each brain area. The voxels in higher visual areas showed larger estimated RFs than those in lower visual areas. Then, the ball's position in a separate session was predicted by maximum likelihood estimation using the RF models of individual voxels. We also tested a model-free multivoxel regression (support vector regression, SVR) to predict the position. We found that regardless of the difference in RF size, all visual areas showed similar prediction accuracies, especially on the horizontal dimension. Higher areas showed slightly lower accuracies on the vertical dimension, which appears to be attributed to the narrower spatial distributions of the RF centers. The results suggest that much position information is preserved in population activity through the hierarchical visual pathway regardless of RF sizes and is potentially available in later processing for recognition and behavior.
高级视觉区域的神经元与低级视觉区域相比，对更复杂的视觉特征具有更广泛的感受野（RF）。因此，一般认为高级视觉区域携带的关于视野中可见物体位置的信息较少。然而，更大的 RF 并不意味着在群体水平上失去了位置信息。在这里，我们评估了从具有不同 RF 大小的六个代表性视觉区域中的每个区域的活动模式中，物体的位置（解码）可以被多准确地预测。我们收集了功能磁共振成像（fMRI）反应，同时人类被试观察一个球在二维场中随机移动。为了估计个体 fMRI 体素的群体 RF 大小，对每个大脑区域的个体体素进行了 RF 模型拟合。与低级视觉区域相比，高级视觉区域的体素显示出更大的估计 RF。然后，使用个体体素的 RF 模型通过最大似然估计来预测球在下一次试验中的位置。我们还测试了一种无模型的多体素回归（支持向量回归，SVR）来预测位置。我们发现，无论 RF 大小的差异如何，所有视觉区域都表现出相似的预测准确性，尤其是在水平维度上。高级区域在垂直维度上的准确率略低，这似乎归因于 RF 中心的空间分布较窄。结果表明，通过分层视觉通路，大量的位置信息在群体活动中得以保留，而与 RF 大小无关，并且在后续的识别和行为处理中可能是可用的。
