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Examining simultaneous encoding of local and remote space across distinct hippocampal subnetworks

Examining simultaneous encoding of local and remote space across distinct hippocampal subnetworks
检查跨不同海马子网络的本地和远程空间的同步编码
批准号:
10471801
负责人:
Rhino Nevers
金额:
$4.33万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-04-01 至 2025-03-31

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中文摘要
翻译
项目总结/摘要 当前的经历、记忆和想象中的未来都会影响行为。证据表明, 海马体(HPC)可以表示过去的位置、现在的位置和可能的未来轨迹。HPC, 然而,通常被称为一个单一的结构,一次只能有一种类型的表示,但 HPC沿其背腹轴可分为沿着亚区。有证据表明,不同的职能作用 的背侧和腹侧HPC的空间导航,但最近的基因表达研究表明, 背腹轴至少可分为三个亚区:背侧、中间和腹侧。相比 在这些分区中,电生理特征沿着HPC纵轴逐渐演变,表明 功能的逐步演变。背侧HPC(dHPC)和中间HPC(iHPC)是否表现为 由分子细分或作为更大功能梯度的一部分所暗示的不同网络是未知的。 已知这两个子区域都代表空间,并且dHPC中的研究表明, 局部和非局部空间在时间上围绕场电位中的规则振荡形成图案。这 振荡存在,但在iHPC中相移,因此局部和非局部的时间模式 表示可以不同于dHPC中的图案化。这意味着HPC可以同时 代表过去、现在和未来的位置,如果是这样,这两个次区域应该被视为不同的 这两个网络都有助于编码空间。通过实验和分析相结合 方法,该项目测试的假设,dHPC和iHPC是最好的理解为不同的子区域, 不相干地表示空间。 我们将通过在大鼠dHPC和iHPC中同时记录在空间时间间隔期间的时间来测试这些假设。 导航任务和研究空间表征的结构和时间组织(目标1)以及 作为两个分区域之间相关活动的特征,使用网络活动模型(Aim 2)。这种计算模型作为一种补充的方法来理解相关的性质, 活动的HPC;它是完全不可知的神经元可以编码,并没有自由参数, 调整。通过将这两种实验方法与尖端分析技术相结合, 有可能揭示HPC中以前未知的丰富表示, 洞察未来的记忆和模拟如何与当前的经验相协调。的 这些表征的同时存在可以作为将过去,现在, 和未来的经历传递给彼此
英文摘要
Project Summary/Abstract Current experiences, memories, and imagined futures all affect behavior. Evidence demonstrates that the hippocampus (HPC) can represent past locations, present position, and possible future trajectories. The HPC, however, is often referred to as a single structure capable of only one type of representation at a time, but the HPC can be divided into subregions along its dorsoventral axis. There is evidence for different functional roles of the dorsal and ventral HPC in spatial navigation, but recent gene expression studies suggest the dorsoventral axis can be divided into at least three subregions: dorsal, intermediate, and ventral. In contrast to these partitions, electrophysiology signatures evolve gradually along the HPC longitudinal axis, suggesting a gradual evolution of function. Whether the dorsal HPC (dHPC) and intermediate HPC (iHPC) behave as distinct networks as suggested by molecular subdivisions or as parts of a larger functional gradient is unknown. Both subregions are known to represent space, and studies in the dHPC have shown that representations of local and nonlocal space are temporally patterned around a regular oscillation in the field potential. This oscillation is present but phase-shifted in the iHPC, so the temporal patterning of local and nonlocal representations may differ from the patterning in the dHPC. This means that the HPC may simultaneously represent past, present, and future locations, and if so, the two subregions should be viewed as distinct networks that both contribute to encoding space. Through a combination of experimental and analytic methods, this project tests the hypothesis that dHPC and iHPC are best understood as distinct subregions that represent space noncoherently. We will test these hypotheses by recording simultaneously in the rat dHPC and iHPC during a spatial navigation task and studying the structure and temporal organization of spatial representations (Aim 1) as well as the characteristics of correlated activity across the two subregions using a model of network activity (Aim 2). This computational model serves as a complementary approach to understanding the nature of correlated activity in the HPC; it is completely agnostic to what neurons may encode and has no free parameters to adjust. By combining both experimental methods with cutting edge analytic techniques, the proposed aims have the potential to uncover previously unknown richness of representations in the HPC that will give further insight into how memories and simulations of the future are coordinated with present experiences. The simultaneous existence of these representations may serve as a neural substrate for relating past, present, and future experiences to one another across time.
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Examining simultaneous encoding of local and remote space across distinct hippocampal subnetworks
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