Peking University School of Life Sciences, 100871, Beijing, China.
Peking-Tsinghua Center for Life Sciences, 100871, Beijing, China.
Nat Commun. 2020 Feb 4;11(1):697. doi: 10.1038/s41467-020-14501-y.
The integration of synaptic inputs onto dendrites provides the basis for neuronal computation. Whereas recent studies have begun to outline the spatial organization of synaptic inputs on individual neurons, the underlying principles related to the specific neural functions are not well understood. Here we perform two-photon dendritic imaging with a genetically-encoded glutamate sensor in awake monkeys, and map the excitatory synaptic inputs on dendrites of individual V1 superficial layer neurons with high spatial and temporal resolution. We find a functional integration and trade-off between orientation-selective and color-selective inputs in basal dendrites of individual V1 neurons. Synaptic inputs on dendrites are spatially clustered by stimulus feature, but functionally scattered in multidimensional feature space, providing a potential substrate of local feature integration on dendritic branches. Furthermore, apical dendrite inputs have larger receptive fields and longer response latencies than basal dendrite inputs, suggesting a dominant role for apical dendrites in integrating feedback in visual information processing.
树突上的突触输入整合为神经元计算提供了基础。虽然最近的研究已经开始概述单个神经元上突触输入的空间组织，但与特定神经功能相关的基本原理尚不清楚。在这里，我们在清醒的猴子中进行了双光子树突成像，并以高时空分辨率绘制了单个 V1 浅层神经元树突上的兴奋性突触输入。我们发现单个 V1 神经元的基底树突上的方向选择性和颜色选择性输入之间存在功能整合和权衡。树突上的突触输入在刺激特征上是空间聚类的，但在多维特征空间中是功能分散的，为树突分支上的局部特征整合提供了潜在的基质。此外，与基底树突输入相比，树突上的输入具有更大的感受野和更长的反应潜伏期，这表明树突在整合视觉信息处理中的反馈方面起着主导作用。
