• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IBRO Reports >A simplified morphological classification scheme for pyramidal cells in six layers of primary somatosensory cortex of juvenile rats
【24h】

A simplified morphological classification scheme for pyramidal cells in six layers of primary somatosensory cortex of juvenile rats

机译：幼年大鼠体感皮层六层锥体细胞的简化形态分类方案

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The majority of neurons in the neocortex are excitatory pyramidal cells (PCs). Many systematic classification schemes have been proposed based the neuronal morphology, the chemical composition, and the synaptic connectivity,etc. Recently, a cortical column of primary somatosensory cortex (SSC) has been reconstruction and functionally simulated (Markram et al., 2015). Putting forward from this study, here we proposed a simplified classification scheme for PCs in all layers of the SSC by mainly identifying apical dendritic morphology based on a large data set of 3D neuron reconstructions. We used this scheme to classify three types in layer 2, two in layer 3, three in layer 4, four in layer 5, and six types in layer 6. These PC types were visually distinguished and confirmed by quantitative differences in their morphometric properties. The classes yielded using this scheme largely corresponded with PC classes that were defined previously based on other neuronal and synaptic properties such as long-range projects and synaptic innervations, further validating its applicability. Therefore, the morphology information of apical dendrites is sufficient for a simple scheme to classify a spectrum of anatomical types of PCs in the SSC.
机译：新皮层中的大多数神经元是兴奋性锥体细胞（PCs）。根据神经元的形态，化学组成和突触的连通性等，提出了许多系统的分类方案。最近，主要的体感皮层（SSC）的皮质柱已被重建并进行了功能模拟（Markram et al。，2015）。从这项研究提出来的是，在此我们通过主要基于3D神经元重建的大型数据集识别根尖树突形态，提出了SSC所有层中PC的简化分类方案。我们使用此方案对第2层中的三种类型进行了分类，第2层中的三种，第3层的第4层，第4层的第5层，以及第6层中的了6种类型。这些PC类型在视觉上是可区分的，并通过其形态特征的定量差异进行了确认。使用该方案产生的类别在很大程度上与先前基于其他神经元和突触特性（例如远程项目和突触神经支配）定义的PC类相对应，从而进一步验证了其适用性。因此，顶端树突的形态学信息足以用于对SSC中PC的解剖学类型进行分类的简单方案。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号