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

大脑的一个中心功能是创造来自外部的刺激和经验的内部表征 世界引导行为。在这里，我们研究了神经表征的电路机制， 外部空间，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。空间表征的神经基础部分取决于大脑中的回路。 内侧内嗅皮层（MEC），其中包含编码空间位置，方向和运行的神经元 动物的速度。在不同的环境中，位置和方向细胞的激发场可以改变 它们的发射率和旋转或移动到一个新的空间位置-称为“重新映射”的现象。连同 在海马旁区的其他结构中，MEC神经元可以产生独特的神经表征， 不同的环境，可能有助于不同的上下文或情节的编码。在重新映射时， 在MEC中，经常在感觉特征不同的环境（即视觉或气味线索）之间进行研究， 我们在最近的初步数据中发现，行为变量（即跑步速度，对奖励的期望） 可以引起MEC中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神经群体状态的转变： 内嗅回路中的关键节点（目标1）和行为驱动的MEC空间图被优化， 表示与环境中执行的导航行为相关的特征（目标3）。此外，我们旨在 建立行为变量变化与MEC神经群体状态转变之间的因果关系 (Aim 2）。为了实现这些目标，我们建议将使用硅探针的联合收割机电生理学与空间 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，导致缺乏研究认为MEC神经编码在人口水平或作为 行为变量的函数。然而，新版本的硅探针使我们能够记录数百个 的MEC神经元同时沿着几乎整个长度的小鼠内嗅皮层。再加上 虚拟现实任务可以提供感官和行为变量以及光遗传学的密集采样 扰动，以建立行为变量变化与MEC神经元转换之间的因果关系 人口状态，将使我们能够实现重要的新的洞察机制的转变， MEC神经群体状态和这种过渡在支持记忆和导航中的作用。

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.