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 主演：丽莎·乔科莫博士 项目摘要 在Rp4(导航和内存)中，我们利用导航系统进行实验研究 外部感觉输入和内部网络动力学的理论和计算原理 不同的大脑状态相互作用，产生导航所需的神经计算。我们主要关注四个方面 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)、内侧嗅觉皮质(MEC)、海马区(HPC)和脾后皮质(RSC)。在我们的第一次 目的，我们考虑内部动力如何与外部感觉输入相互作用以产生统一的感知 位置。我们利用虚拟现实和NeuroPixel硅探测器的高密度记录能力 明确测试在RP3中开发的贝叶斯线索集成框架(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，我们考虑 行为状态的变化如何影响空间神经映射的稳定性和测试理论原理， 在RP3中开发，并应用于RP1(动机和感知)和RP2(灵长类决定)中的V1 自发活动如何影响对大脑中微弱感觉输入的检测或放大 导航电路。与目标1中一样，我们利用虚拟现实和高密度记录功能 神经像素硅探头。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，通过神经元的空间放电模式来衡量，跨环境和参数 不同的外部标志性力量(提示丰富或提示较差的条件)。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上建立因果关系- 导航仪。通过应用RP1中开发的多SLM双光子钙离子成像方法，该方法能够 用于实时可视化和控制细胞系综的广视场光学接入，我们将测试理论 在RP2中提出了关于吸引子状态和关键的可兴奋区域是否能够驱动行为， 已经在V1中观察到的，存在于非感觉皮质区域。在这里，外部输入是通过 用于编码内部位置估计的光遗传学和内在网络动力学的单细胞分辨率 用2P钙离子显像法测量。一起，跨越我们所有的目标，我们调查多个 V1旁边与航行相关的大脑区域将允许我们严格考虑 在RP3中开发的神经计算类的基本理论遵循 根据不同的大脑回路对内部动力学的权重与 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.