Hatanaka Gaku, Inagaki Mikio, Takeuchi Ryosuke F, Nishimoto Shinji, Ikezoe Koji, Fujita Ichiro
Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, 565-0871, Japan.
Center for Information and Neural Networks, Osaka University and National Institute of Information and Communications Technology, Suita, Osaka, 565-0871, Japan.
Brain Struct Funct. 2022 May;227(4):1385-1403. doi: 10.1007/s00429-022-02468-z. Epub 2022 Mar 14.
Natural scenes are characterized by diverse image statistics, including various parameters of the luminance histogram, outputs of Gabor-like filters, and pairwise correlations between the filter outputs of different positions, orientations, and scales (Portilla-Simoncelli statistics). Some of these statistics capture the response properties of visual neurons. However, it remains unclear to what extent such statistics can explain neural responses to natural scenes and how neurons that are tuned to these statistics are distributed across the cortex. Using two-photon calcium imaging and an encoding-model approach, we addressed these issues in macaque visual areas V1 and V4. For each imaged neuron, we constructed an encoding model to mimic its responses to naturalistic videos. By extracting Portilla-Simoncelli statistics through outputs of both filters and filter correlations, and by computing an optimally weighted sum of these outputs, the model successfully reproduced responses in a subpopulation of neurons. We evaluated the selectivities of these neurons by quantifying the contributions of each statistic to visual responses. Neurons whose responses were mainly determined by Gabor-like filter outputs (low-level statistics) were abundant at most imaging sites in V1. In V4, the relative contribution of higher order statistics, such as cross-scale correlation, was increased. Preferred image statistics varied markedly across V4 sites, and the response similarity of two neurons at individual imaging sites gradually declined with increasing cortical distance. The results indicate that natural scene analysis progresses from V1 to V4, and neurons sharing preferred image statistics are locally clustered in V4.
自然场景具有多样的图像统计特征，包括亮度直方图的各种参数、类Gabor滤波器的输出，以及不同位置、方向和尺度的滤波器输出之间的成对相关性（波蒂利亚 - 西蒙切利统计量）。其中一些统计量捕捉了视觉神经元的响应特性。然而，尚不清楚这些统计量在多大程度上能够解释神经元对自然场景的反应，以及调谐到这些统计量的神经元在整个皮层中是如何分布的。我们使用双光子钙成像和编码模型方法，在猕猴视觉区域V1和V4中解决了这些问题。对于每个成像神经元，我们构建了一个编码模型来模拟其对自然主义视频的反应。通过滤波器输出和滤波器相关性提取波蒂利亚 - 西蒙切利统计量，并计算这些输出的最优加权和，该模型成功地再现了一部分神经元的反应。我们通过量化每个统计量对视觉反应的贡献来评估这些神经元的选择性。其反应主要由类Gabor滤波器输出（低层次统计量）决定的神经元在V1的大多数成像位点都很丰富。在V4中，高阶统计量（如跨尺度相关性）的相对贡献增加。在V4的不同位点，偏好的图像统计量有显著差异，并且在单个成像位点，两个神经元的反应相似性随着皮层距离的增加而逐渐下降。结果表明，自然场景分析从V1发展到V4，并且共享偏好图像统计量的神经元在V4中局部聚集。
