The Dynamics of Neural Representations for Distinct Spatial Contexts and Memory Episodes

大脑的核心功能是创造来自外部的刺激和经验的内部表征 引导行为的世界。在这里，我们检查了潜在的神经表示的电路机制 外部空间，一种对空间记忆和导航至关重要的表征，并受到神经退行性疾病的影响 和精神疾病。表示空间的神经基础在一定程度上取决于 内侧内嗅皮层(MEC)，它包含编码空间位置、方向和跑步的神经元 动物的速度。在不同的环境之间，位置单元和定向单元的激发范围可以改变 它们的射速和旋转或移动到一个新的空间位置--这种现象被称为“重新映射”。与.一起 在海马区的其他结构中，MEC神经元可以产生独特的神经表征 不同的环境，可能有助于对不同的背景或情节进行编码。在重新映射时 在MEC中经常被研究在感觉特征(即视觉或气味提示)不同的环境之间， 我们在最近和初步的数据中发现，行为变量(如跑步速度、对奖励的期望) 可以在MEC中引起神经群体状态之间的内部转换(即重新映射)。在这里，我们的目标是测试 假设行为变量的变化可以通过以下方式驱动MEC神经种群状态的转变 内嗅路中的关键节点(目标1)和行为驱动的MEC空间地图被优化以 表示与在环境中执行的导航行为相关的特征(目标3)。此外，我们的目标是 建立行为变量的变化和MEC神经种群状态转变之间的因果关系 (目标2)。为了解决这些目标，我们建议将使用硅探针的电生理学与空间和 表现良好的老鼠的记忆任务。到目前为止，电生理学方法不得不与有限的记录作斗争。 通道计数，导致缺乏将MEC神经编码视为种群水平或AS的研究 行为变量的函数。然而，新版本的硅探测器使我们能够记录数百个 几乎在小鼠内嗅觉皮质的整个长度上同时显示MEC神经元。这一点，与 虚拟现实任务，可以提供对感觉和行为变量的密集采样，以及光遗传 微扰建立行为变量变化和MEC神经转换之间的因果关系 人口状态，将使我们能够获得重要的新见解，以潜在的过渡机制 MEC神经种群状态以及这种转换在支持记忆和导航方面的作用。

A central function of the brain is to create internal representations of stimuli and experiences from the outside world to guide behavior. Here, we examine the circuit mechanisms underlying the neural representation of external space, a representation essential to spatial memory and navigation, and impacted by neurodegenerative and psychiatric diseases. The neural basis for the representation of space depends, in part, on circuits in the medial entorhinal cortex (MEC), which contains neurons that encode the spatial position, orientation and running speed of an animal. Between distinct environments, the firing fields of position and orientation cells can change their firing rate and rotate or move to a new spatial location – phenomenon known as ‘remapping’. Together with other structures in the parahippocampal region, MEC neurons can generate unique neural representations for distinct environments, potentially contributing to the encoding of different contexts or episodes. While remapping in MEC has often been studied between environments that differ in sensory features (i.e. visual or odor cues), we have found in recent and preliminary data that behavioral variables (i.e. running speed, expectation of reward) can evoke internal transitions between neural population states (i.e. remapping) in MEC. Here, we aim to test the hypotheses that a change in behavioral variables can drive transitions in MEC neural population states via key nodes in entorhinal circuitry (Aim 1) and that behaviorally driven MEC spatial maps are optimized to represent features relevant to the navigational behavior executed in the environment (Aim 3). Moreover, we aim to establish causality between changes in behavioral variables and transitions in MEC neural population states (Aim 2). To address these aims, we propose to combine electrophysiology using silicon probes with spatial and memory tasks in behaving mice. Until now, electrophysiological approaches had to contend with limited recording channel counts, contributing to a lack of studies that considered MEC neural coding at the population level or as a function of behavioral variables. However, new versions of silicon probes have allowed us to record hundreds of MEC neurons simultaneously along nearly the entire length of mouse entorhinal cortex. This, combined with virtual reality tasks that can provide dense sampling of sensory and behavioral variables, as well as optogenetic perturbations to establish causality between changes in behavioral variables and transitions in MEC neural population states, will enable us to achieve significant new insight into the mechanisms underlying transitions in MEC neural population states and the of such transitions in supporting memory and navigation.