Grid Cells and Precession in a Biophysical Entorhinal-Hippocampal Network Model

描述（由申请人提供）：本提案将研究内侧内嗅皮质-海马网络的生物学特性，使其能够执行空间处理功能。这项研究将集中在网格细胞形成和θ相位进动的生物学机制。网格细胞是内侧内嗅皮层中的空间敏感细胞，每当动物位于该细胞的发射场时，该细胞就会以六边形阵列的位置在环境中发射。内嗅皮层中的网格细胞和海马中的位置细胞之间的相互作用被认为在整体空间功能中非常重要，例如编码动物的位置和规划未来的路径。θ相位进动是一种发生在位置细胞（空间敏感细胞，每当动物处于环境的某个位置时就会激发）中的现象。位置细胞相对于网络θ节律振荡改变其放电相位，这表明细胞群体组织其活动以允许代表轨迹的压缩神经活动。这种表示对于在记忆中编码轨迹和规划未来轨迹可能是有用的。这项研究将使用一个大规模的，生物约束模型的内侧内嗅皮层-海马网络，以评估一个生物现实的网络，以支持各种模型的网格细胞形成和θ相位进动，以前提出的能力。从这项工作中获得的知识将使人们更广泛地了解各种神经系统疾病（如癫痫和阿尔茨海默病）可能对空间处理和记忆等认知功能产生不利影响的机制。此外，这项工作产生的计算模型将免费共享，并可用于搜索和量化各种疾病的潜在治疗靶点。

DESCRIPTION (provided by applicant): This proposal will study the biological properties of the medial entorhinal cortical-hippocampal network that enable it to perform spatial processing functions. This research will focus on biological mechanisms of grid cell formation and theta phase precession. Grid cells are spatially sensitive cells in medial entorhinal cortex that fire whenever an animal is positioned in the firing fields of the cell, in a hexagonal array of position in the environment. Interactions between grid cells in entorhinal cortex and place cells in the hippocampus are thought to be important in overall spatial functions, such as coding the position of the animal and planning future paths. Theta phase precession is a phenomenon that occurs in place cells (spatially sensitive cells that fire whenever an animal is in a certain locaton of an environment). Place cells shift their phase of firing relative to network theta rhythm oscillations in a manner suggesting that populations of cells organize their activity to allow for compressed neural activity representing a trajectory. This representation could be useful for encoding of trajectories in memory and planning of future trajectories. This research will use a large scale, biologically constrained model of the medial entorhinal cortical- hippocampal network to assess the ability of a biologically realistic network to support the various models of grid cell formation and theta phase precession that have been previously proposed. The knowledge gained from this work will allow a broader understanding of the mechanisms by which various neurological disorders such as epilepsy and Alzheimer's disease can adversely affect cognitive functions such as spatial processing and memory. Further, the computational model produced by this work will be freely shared and can be used to search for and quantify potential therapeutic targets for various disorders.