Research Project 4 - Internal state dynamics of navigation and memory

研究项目4 -导航和记忆的内部状态动力学 领导：丽莎Giocomo博士 项目摘要 在RP 4（导航和记忆）中，我们利用导航系统进行实验研究 理论和计算原理如何外部感官输入和内部网络动态，跨 不同的大脑状态相互作用，产生导航所需的神经计算。我们专注于四个 为小鼠视觉导航提供补充计算的大脑区域：初级视觉 皮质（V1）、内侧内嗅皮质（MEC）、海马（HPC）和压后皮质（RSC）。在我们的第一 目的，我们考虑内部动态如何与外部感官输入相互作用，以产生一个统一的感知， 位置我们利用虚拟现实和Neuropixel硅探头的高密度记录功能， 明确测试预测的贝叶斯线索整合框架开发的RP 3（理论和 内部状态动力学的计算）。在这里，内部动力反映了内在的路径整合 计算，而外部输入包括视觉地标和光流输入。在我们的第二个目标中，我们认为 行为状态的变化如何影响空间神经地图的稳定性，并测试理论原则， 在RP 3中开发并应用于RP 1（动机和感知）和RP 2（灵长类决策）中的V1 制作），自发活动如何影响检测或放大微弱的感觉输入， 导航电路与目标1一样，我们利用虚拟现实和 神经像素硅探针。在这里，我们认为自发活动的变化类似于 内部行为状态（饱腹感或唤醒），并考虑这如何影响内部位置的稳定性 估计，如通过神经元的空间放电模式测量的，在具有参数化的环境中， 不同的外部标志强度（线索丰富或线索贫乏条件）。在我们的第三个目标中，我们考虑是否 驱动单个神经元的活动可以在RSC中的神经表征和视觉表征之间建立因果关系， 导航。通过应用RP 1中开发的MultiSLM双光子Ca 2+成像方法， 宽视场光学访问可视化和控制细胞系综在真实的时间，我们将测试理论 在RP 2中开发的关于吸引子状态以及是否能够驱动行为的临界可兴奋状态， 在V1中观察到的，存在于非感觉皮层区域。在这里，外部输入是用 单细胞分辨率光遗传学和编码内部位置估计的内在网络动力学 使用2 P Ca 2+成像测量。总之，在我们所有的目标，我们的方法，调查多个 与V1相关的导航脑区将使我们能够严格考虑 神经计算类的基础理论-在RP 3中开发-遵循通用原则， 大脑皮层区域或显示分歧的基础上如何不同的大脑回路权重内部动态与 外部输入。

Research Project 4 – Internal state dynamics of navigation and memory Lead: Lisa Giocomo PhD Project Summary In RP4 (Navigation and memory) we leverage the navigation system to experimentally investigate theoretical and computational principles for how external sensory inputs and internal network dynamics, across different brain states, interact to generate the neural computations necessary for navigation. We focus on four brain regions that provide complementary computations for visually guided navigation in mice: primary visual cortex (V1), medial entorhinal cortex (MEC), hippocampus (HPC) and retrosplenial cortex (RSC). In our first aim, we consider how internal dynamics interact with external sensory inputs to generate a unified percept of position. We leverage virtual reality and the high density recording capabilities of Neuropixel silicon probes to explicitly test predictions of a Bayesian cue integration framework developed in RP3 (Theory and computation of internal state dynamics). Here, internal dynamics reflect intrinsic path integration calculations, while external inputs include visual landmark and optic flow inputs. In our second aim, we consider how a change in behavioral state impacts the stability of neural maps of space and test theoretical principles, developed in RP3 and applied to V1 in RP1 (Motivation and perception) and RP2 (Primate decision making), for how spontaneous activity influences the detection or amplification of weak sensory inputs in the navigation circuit. As in Aim 1, we leverage virtual reality and the high density recording capabilities of Neuropixel silicon probes. Here, we consider a change in spontaneous activity as analogous to a change in internal behavioral state (satiety or arousal) and consider how this impacts the stability of internal position estimates, as measured by the spatial firing patterns of neurons, across environments with parametrically differing external landmark strength (cue rich or cue poor conditions). In our third aim, we consider whether driving the activity of single neurons can establish causality between neural representations in RSC and visually- guided navigation. By applying a MultiSLM 2-photon Ca2+ imaging method developed in RP1, which enables wide-field optical access for visualization and control of cellular ensembles in real time, we will test theories developed in RP2 regarding attractor states and whether critically excitable regimes capable of driving behavior, which have been observed in V1, exist in non-sensory cortical regions. Here, external input is manipulated with single-cell resolution optogenetically, and intrinsic network dynamics for encoding internal position estimates measured using 2P Ca2+ imaging. Together, across all of our aims, our approach of investigating multiple navigationally relevant brain regions alongside V1 will allow us to rigorously consider the degree to which foundational theories for classes of neural computation – developed in RP3 – follow universal principles across cortical regions or show divergence based on how disparate brain circuits weight internal dynamics versus external inputs.