Spatial Firing Correlates of Physiologically Distinct Cell Types of the Rat Dentate Gyrus
Spatial Firing Correlates of Physiologically Distinct Cell Types of the Rat Dentate Gyrus
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DOI：
10.1523/jneurosci.6038-11.2012
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发表时间：
2012-03-14
影响因子：
5.3
通讯作者：
Knierim, James J.
中科院分区：
文献类型：
--
作者：
Neunuebel, Joshua P.;Knierim, James J.
齿状回（DG）在通过海马的信息流中占据关键位置。其主要细胞，颗粒细胞，具有空间选择性的位置字段。然而，位于大鼠齿状回门的细胞的行为相关性是未知的。我们在这里报告，颗粒层以下的细胞显示空间选择性放电，由多个子字段。从DG记录的其他细胞具有单一位置字段。与具有多个场的细胞相比，在睡眠期间以较低速率发射的单场细胞的突发性较小，并且更有可能与大量在睡眠期间活跃而在行为期间沉默的神经元同时被记录。我们建议，细胞与单字段很可能是成熟的颗粒细胞，使用稀疏编码，以潜在地消除歧义输入模式。此外，我们推测具有多个视野的细胞可能是门细胞或新生颗粒细胞。这些数据是第一次演示，基于生理标准，单和多领域的细胞构成至少两个不同的细胞类中的DG。由于放电相关和细胞类型的DG的异质性，了解哪些细胞类型对应于哪些放电模式，以及这些相关如何随着行为状态和不同环境之间的变化，是测试长期存在的计算理论的关键问题，即DG使用非常稀疏的编码策略执行模式分离功能。
The dentate gyrus (DG) occupies a key position in information flow through the hippocampus. Its principal cell, the granule cell, has spatially selective place fields. However, the behavioral correlates of cells located in the hilus of the rat dentate gyrus are unknown. We report here that cells below the granule layer show spatially selective firing that consists of multiple subfields. Other cells recorded from the DG had single place fields. Compared with cells with multiple fields, cells with single fields fired at lower rates during sleep were less bursty, and were more likely to be recorded simultaneously with large populations of neurons that were active during sleep and silent during behavior. We propose that cells with single fields are likely to be mature granule cells that use sparse encoding to potentially disambiguate input patterns. Furthermore, we hypothesize that cells with multiple fields might be cells of the hilus or newborn granule cells. These data are the first demonstration, based on physiological criteria, that single-and multiple-field cells constitute at least two distinct cell classes in the DG. Because of the heterogeneity of firing correlates and cell types in the DG, understanding which cell types correspond to which firing patterns, and how these correlates change with behavioral state and between different environments, are critical questions for testing long-standing computational theories that the DG performs a pattern separation function using a very sparse coding strategy.