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

• 批准号: 10490244
• 负责人: Lisa Giocomo
• 金额: $ 58.43万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10490244
• 关键词: Algorithms; Arousal; Automobile Driving; Bayesian Analysis; Behavior; Behavioral; Behavioral Paradigm; Brain; Brain region; Cells; Code; Collaborations; Complement; Complex; Coupled; Cues; Data; Data Science Core; Decision Making; Detection; Development; Discrimination; Doctor of Philosophy; Electrophysiology (science); Environment; Etiology; Foundations; Hippocampus (Brain); Image; Individual; Lead; Maps; Measurement; Measures; Medial; Memory; Motivation; Movement; Mus; Nature; Navigation System; Neurons; Optics; Pattern; Perception; Population; Positioning Attribute; Primates; Research Project Grants; Resolution; Rodent; Satiation; Sensory; Silicon; Techniques; Testing; Time; Uncertainty; Visual; Visualization; Weight; Work; alertness; area striata; base; computer framework; density; driving behavior; entorhinal cortex; flexibility; imaging modality; network models; neural circuit; novel; optic flow; optogenetics; predictive modeling; recruit; relating to nervous system; sensory input; spatiotemporal; theories; two-photon; virtual; virtual reality; virtual reality environment; way finding

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.

研究项目4 - 导航和内存的内部状态动态 铅：Lisa Giocomo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调查 外部感觉输入和内部网络动态如何，跨越的理论和计算原理 不同的大脑状态相互作用以生成导航所需的神经计算。我们专注于四个 为小鼠视觉引导导航提供完整计算的大脑区域：主要视觉 皮层（V1），中位enorthinal Cortex（MEC），海马（HPC）和泛胞后皮质（RSC）。在我们的第一个 目的，我们考虑内部动力学如何与外部感觉输入相互作用，以产生对 位置。我们利用虚拟现实和神经硅硅的高密度记录功能 明确测试RP3中开发的贝叶斯提示整合框架的预测（理论和 内部状态动力学的计算）。在这里，内部动力学反映了内在的路径积分 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图和测试理论原理的稳定性， 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 制作），关于赞助活动如何影响弱感觉输入的检测或扩增 导航电路。与AIM 1一样，我们利用虚拟现实和高密度记录功能 Neuropixel硅问题。在这里，我们认为赞助活动的变化类似于变化 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 通过具有参数的环境，通过神经元的空间触发模式来衡量的估计值 不同的外部地标强度（提示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性可以在RSC和视觉上建立神经元之间的因果关系 - 导航。通过应用RP1中开发的Multislm 2-Photon Ca2+成像方法，该方法可以启用 宽场光学访问可视化和控制蜂窝合奏的实时，我们将测试理论 在RP2中开发的有关吸引子状态以及是否能够驾驶行为的令人兴奋的政权， 在V1中观察到的，存在于非感官皮质区域。在这里，外部输入被操纵 单细胞分辨率在光遗传学上以及用于编码内部位置估计的固有网络动力学 使用2p Ca2+成像测量。在我们所有的目标中，我们研究多个目标的方法 与V1一起进行导航相关的大脑区域将使我们能够严格考虑以下程度 神经计算类的基础理论 - 在RP3中开发 - 遵循跨越的普遍原理 皮质区域或根据不同的脑电路体重内部动力学而与 外部输入。