Department of Computer Science, University of Tsukuba, Tsukuba, Japan.
Neural Computation Unit, Okinawa Institute of Science and Technology, Okinawa, Japan.
PLoS One. 2022 Jun 16;17(6):e0268650. doi: 10.1371/journal.pone.0268650. eCollection 2022.
Neurons in visual area V4 modulate their responses depending on the figure-ground (FG) organization in natural images containing a variety of shapes and textures. To clarify whether the responses depend on the extents of the figure and ground regions in and around the classical receptive fields (CRFs) of the neurons, we estimated the spatial extent of local figure and ground regions that evoked FG-dependent responses (RF-FGs) in natural images and their variants. Specifically, we applied the framework of spike triggered averaging (STA) to the combinations of neural responses and human-marked segmentation images (FG labels) that represent the extents of the figure and ground regions in the corresponding natural image stimuli. FG labels were weighted by the spike counts in response to the corresponding stimuli and averaged over. The bias due to the nonuniformity of FG labels was compensated by subtracting the ensemble average of FG labels from the weighted average. Approximately 50% of the neurons showed effective RF-FGs, and a large number exhibited structures that were similar to those observed in virtual neurons with ideal FG-dependent responses. The structures of the RF-FGs exhibited a subregion responsive to a preferred side (figure or ground) around the CRF center and a subregion responsive to a non-preferred side in the surroundings. The extents of the subregions responsive to figure were smaller than those responsive to ground in agreement with the Gestalt rule. We also estimated RF-FG by an adaptive filtering (AF) method, which does not require spherical symmetry (whiteness) in stimuli. RF-FGs estimated by AF and STA exhibited similar structures, supporting the veridicality of the proposed STA. To estimate the contribution of nonlinear processing in addition to linear processing, we estimated nonlinear RF-FGs based on the framework of spike triggered covariance (STC). The analyses of the models based on STA and STC did not show inconsiderable contribution of nonlinearity, suggesting spatial variance of FG regions. The results lead to an understanding of the neural responses that underlie the segregation of figures and the construction of surfaces in intermediate-level visual areas.
V4 区神经元根据自然图像中的图形-背景（FG）组织来调节其反应，这些自然图像包含各种形状和纹理。为了阐明神经元的反应是否取决于其经典感受野（CRF）内及周围的图形和背景区域的范围，我们估计了在自然图像及其变体中诱发 FG 依赖反应（RF-FG）的局部图形和背景区域的空间范围。具体来说，我们将尖峰触发平均（STA）框架应用于神经反应和人类标记的分割图像（FG 标签）的组合，这些图像代表相应自然图像刺激中图形和背景区域的范围。FG 标签根据对应刺激的尖峰计数进行加权，并进行平均。通过从加权平均值中减去 FG 标签的集合平均值来补偿 FG 标签的非均匀性引起的偏差。大约 50%的神经元表现出有效的 RF-FG，并且大量神经元表现出与具有理想 FG 依赖反应的虚拟神经元中观察到的结构相似的结构。RF-FG 的结构表现为在 CRF 中心周围对优选侧（图形或背景）有反应的子区域，以及在周围对非优选侧有反应的子区域。对图形有反应的子区域的范围小于对地面有反应的子区域，这与格式塔规则一致。我们还通过不需要刺激的球形对称（白化）的自适应滤波（AF）方法来估计 RF-FG。通过 AF 和 STA 估计的 RF-FG 表现出相似的结构，支持所提出的 STA 的真实性。为了估计除线性处理之外的非线性处理的贡献，我们基于尖峰触发协方差（STC）框架来估计非线性 RF-FG。基于 STA 和 STC 的模型分析没有显示出非线性的可观贡献，这表明 FG 区域的空间方差。这些结果导致对中间水平视觉区域中图形分割和表面构建所依赖的神经反应的理解。
