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