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内嗅皮层中的网格细胞和非网格细胞通过互补编码方案表征空间位置和环境特征。

Grid and Nongrid Cells in Medial Entorhinal Cortex Represent Spatial Location and Environmental Features with Complementary Coding Schemes.

作者信息

Diehl Geoffrey W, Hon Olivia J, Leutgeb Stefan, Leutgeb Jill K

机构信息

Neurobiology Section and Center for Neural Circuits and Behavior, Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.

Neurobiology Section and Center for Neural Circuits and Behavior, Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA 92093, USA.

出版信息

Neuron. 2017 Apr 5;94(1):83-92.e6. doi: 10.1016/j.neuron.2017.03.004. Epub 2017 Mar 23.

Abstract

The medial entorhinal cortex (mEC) has been identified as a hub for spatial information processing by the discovery of grid, border, and head-direction cells. Here we find that in addition to these well-characterized classes, nearly all of the remaining two-thirds of mEC cells can be categorized as spatially selective. We refer to these cells as nongrid spatial cells and confirmed that their spatial firing patterns were unrelated to running speed and highly reproducible within the same environment. However, in response to manipulations of environmental features, such as box shape or box color, nongrid spatial cells completely reorganized their spatial firing patterns. At the same time, grid cells retained their spatial alignment and predominantly responded with redistributed firing rates across their grid fields. Thus, mEC contains a joint representation of both spatial and environmental feature content, with specialized cell types showing different types of integrated coding of multimodal information.

摘要

内侧内嗅皮层(mEC)通过发现网格细胞、边界细胞和头部方向细胞,已被确定为空间信息处理的枢纽。在这里,我们发现,除了这些特征明确的细胞类别外,mEC中其余近三分之二的细胞几乎都可归类为空间选择性细胞。我们将这些细胞称为非网格空间细胞,并证实它们的空间放电模式与奔跑速度无关,且在同一环境中具有高度可重复性。然而,在响应环境特征的操纵时,如盒子形状或盒子颜色,非网格空间细胞会完全重新组织它们的空间放电模式。与此同时,网格细胞保持其空间排列,并主要通过其网格场中重新分布的放电率做出反应。因此,mEC包含空间和环境特征内容的联合表征,其中专门的细胞类型显示出多模态信息的不同类型的整合编码。

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