...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neuron >Sublayer-Specific Coding Dynamics during Spatial Navigation and Learning in Hippocampal Area CA1
【24h】

Sublayer-Specific Coding Dynamics during Spatial Navigation and Learning in Hippocampal Area CA1

机译：海马区CA1在空间导航和学习过程中特定于子层的编码动力学

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The mammalian hippocampus is critical for spatial information processing and episodic memory. Its primary output cells, CA1 pyramidal cells (CA1 PCs), vary in genetics, morphology, connectivity, and electrophysiological properties. It is therefore possible that distinct CA1 PC subpopulations encode different features of the environment and differentially contribute to learning. To test this hypothesis, we optically monitored activity in deep and superficial CA1 PCs segregated along the radial axis of the mouse hippocampus and assessed the relationship between sublayer dynamics and learning. Superficial place maps were more stable than deep during head-fixed exploration. Deep maps, however, were preferentially stabilized during goal-oriented learning, and representation of the reward zone by deep cells predicted task performance. These findings demonstrate that superficial CA1 PCs provide a more stable map of an environment, while their counterparts in the deep sublayer provide a more flexible representation that is shaped by learning about salient features in the environment.
机译：哺乳动物海马体对于空间信息处理和情景记忆至关重要。它的主要输出细胞，即CA1锥体细胞（CA1 PCs），在遗传学，形态，连通性和电生理特性上各不相同。因此，不同的CA1 PC亚群可能会编码环境的不同特征，从而对学习产生不同的影响。为了验证该假设，我们光学监测了沿小鼠海马体径向轴分隔的深层和浅层CA1 PC的活性，并评估了亚层动力学与学习之间的关系。在进行头部固定的探索过程中，浅层位置地图比深层地图更稳定。但是，在面向目标的学习过程中，深度图优先稳定，并且深度单元表示的奖励区域可预测任务性能。这些发现表明，浅层CA1 PC可以提供更稳定的环境图，而深层亚层的PC则可以提供更灵活的表示形式，这些表示形式是通过了解环境中的显着特征而形成的。

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