Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons.
Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons.
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DOI：
10.1523/eneuro.0113-17.2017
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发表时间：
2017-05
期刊：
影响因子：
3.4
通讯作者：
Vogels R
中科院分区：
文献类型：
--
作者：
Kalfas I;Kumar S;Vogels R
对灵长类动物的功能性磁共振成像研究表明，大脑皮层区域被身体的视觉图像强烈激活。这种身体补丁在猕猴中的存在允许表征其单个神经元的刺激选择性。中上级颞沟体（MSB）补丁神经元表现出类似的刺激选择性的自然，阴影，纹理图像相比，他们的轮廓，这表明形状是一个重要的决定因素MSB的反应。在这里，我们检查和建模的形状选择性的单个MSB神经元。我们测量了单个MSB神经元对各种形状的反应，产生了广泛的反应。我们使用了自适应刺激采样程序，根据神经元的反应选择和修改形状。40%的产生最大反应的形状被人类评定为类似动物的形状，但许多MSB神经元的顶部形状并不像身体。我们用一个模型拟合MSB神经元的形状选择性，该模型根据轮廓段的曲率和方向参数化形状，使用基于像素的模型，以及卷积神经网络（CNN）的单元层。CNN的深度卷积层提供了最佳的拟合优度，神经元响应的中位数解释了77%的可解释方差。拟合优度沿着卷积层的层次结构增加，但对于完全连接的层则较低。除了证明使用深度CNN成功建模单个单元的形状选择性之外，数据还表明语义或类别知识仅略微决定单个MSB神经元的形状选择性。
Functional MRI studies in primates have demonstrated cortical regions that are strongly activated by visual images of bodies. The presence of such body patches in macaques allows characterization of the stimulus selectivity of their single neurons. Middle superior temporal sulcus body (MSB) patch neurons showed similar stimulus selectivity for natural, shaded, and textured images compared with their silhouettes, suggesting that shape is an important determinant of MSB responses. Here, we examined and modeled the shape selectivity of single MSB neurons. We measured the responses of single MSB neurons to a variety of shapes producing a wide range of responses. We used an adaptive stimulus sampling procedure, selecting and modifying shapes based on the responses of the neuron. Forty percent of shapes that produced the maximal response were rated by humans as animal-like, but the top shape of many MSB neurons was not judged as resembling a body. We fitted the shape selectivity of MSB neurons with a model that parameterizes shapes in terms of curvature and orientation of contour segments, with a pixel-based model, and with layers of units of convolutional neural networks (CNNs). The deep convolutional layers of CNNs provided the best goodness-of-fit, with a median explained explainable variance of the neurons’ responses of 77%. The goodness-of-fit increased along the convolutional layers’ hierarchy but was lower for the fully connected layers. Together with demonstrating the successful modeling of single unit shape selectivity with deep CNNs, the data suggest that semantic or category knowledge determines only slightly the single MSB neuron’s shape selectivity.