State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; College of Life Sciences, Beijing Normal University, Beijing 100875, China.
Cell Rep. 2022 Aug 16;40(7):111221. doi: 10.1016/j.celrep.2022.111221.
Spatial integration of visual information is an important function in the brain. However, neural computation for spatial integration in the visual cortex remains unclear. In this study, we recorded laminar responses in V1 of awake monkeys driven by visual stimuli with grating patches and annuli of different sizes. We find three important response properties related to spatial integration that are significantly different between input and output layers: neurons in output layers have stronger surround suppression, smaller receptive field (RF), and higher sensitivity to grating annuli partially covering their RFs. These interlaminar differences can be explained by a descriptive model composed of two global divisions (normalization) and a local subtraction. Our results suggest suppressions with cascaded normalizations (CNs) are essential for spatial integration and laminar processing in the visual cortex. Interestingly, the features of spatial integration in convolutional neural networks, especially in lower layers, are different from our findings in V1.
视觉信息的空间整合是大脑的一项重要功能。然而，视觉皮层中用于空间整合的神经计算仍不清楚。在这项研究中，我们记录了清醒猴子 V1 层对具有不同大小光栅补丁和环的视觉刺激的分层反应。我们发现了三个与空间整合相关的重要反应特性，它们在输入和输出层之间有显著差异：输出层中的神经元具有更强的周围抑制、更小的感受野 (RF) 以及对部分覆盖其 RF 的光栅环的更高敏感性。这些层间差异可以用由两个全局划分（归一化）和一个局部减法组成的描述性模型来解释。我们的结果表明，级联归一化 (CN) 的抑制对于视觉皮层的空间整合和分层处理至关重要。有趣的是，卷积神经网络中空间整合的特征，尤其是在较低层，与我们在 V1 中的发现不同。
