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: 10687148
• 负责人: Lisa Giocomo
• 金额: $ 44.33万
• 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/10687148
• 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; Disparate; Doctor of Philosophy; Electrophysiology (science); Environment; Etiology; Hippocampus; Image; Individual; Lead; Maps; Measurement; Measures; Medial; Memory; Motivation; Movement; Mus; Nature; Navigation System; Neurons; Optics; Perception; Population; Positioning Attribute; Primates; Research Project Grants; Resolution; Rodent; Satiation; Sensory; Silicon; Techniques; Testing; Time; Uncertainty; Visual; Visualization; Weight; Work; alertness; area striata; computer framework; density; driving behavior; entorhinal cortex; flexibility; imaging modality; network models; neural; neural circuit; neuronal patterning; novel; optic flow; optogenetics; predictive modeling; recruit; sensory input; spatiotemporal; technology development; 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 – 导航和记忆的内部状态动力学 领导者：丽莎·乔科莫博士 项目概要 在 RP4（导航和记忆）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。 为小鼠视觉引导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）、海马体（HPC）和压后皮质（RSC）。 目标，我们考虑内部动态如何与外部感官输入相互作用以产生统一的感知 我们利用虚拟现实和 Neuropixel 硅探针的高密度记录功能来实现。 明确测试 RP3 中开发的贝叶斯线索集成框架的预测（理论和 内部状态动力学的计算）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的稳定性并测试理论原理， 在 RP3 中开发并应用于 RP1（动机和感知）和 RP2（灵长类决策）中的 V1 制作），了解自发活动如何影响微弱感觉输入的检测或放大 与目标 1 一样，我们利用虚拟现实和高密度记录功能。 在这里，我们将自发活动的变化视为类似于神经像素的变化。 内部行为状态（饱腹感或觉醒）并考虑这如何影响内部位置的稳定性 通过神经元的空间放电模式测量，在具有参数的环境中进行估计 不同的外部地标强度（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 和视觉中的神经表征之间建立因果关系 通过应用 RP1 中开发的 MultiSLM 2 光子 Ca2+ 成像方法，可以实现引导导航。 用于实时可视化和控制细胞群的宽视场光学访问，我们将测试理论 RP2 中关于吸引子状态以及临界兴奋状态是否能够驱动行为的发展， 在 V1 中观察到的这些现象存在于非感觉皮层区域，在这里，外部输入是通过操纵的。 单细胞光遗传学分辨率和用于编码内部位置估计的内在网络动力学 使用 2P Ca2+ 成像进行测量，综合我们所有的目标，我们研究多种方法。 与 V1 一起的与导航相关的大脑区域将使我们能够严格考虑 神经计算类别的基础理论——在 RP3 中开发——遵循跨领域的通用原则 皮质区域或根据不同的大脑回路如何权衡内部动态与 外部输入。