CRCNS: Dynamics of Gain Recalibration in the Hippocampal-Entorhinal Path Integration System

CRCNS：海马-内嗅路径集成系统中增益重新校准的动力学

• 批准号: 10380673
• 负责人: Noah John Cowan
• 金额: $ 33.9万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10380673
• 关键词: Address; Affect; Agreement; Animals; Augmented Reality; Behavior; Brain; Cells; Cognitive; Complex; Coupling; Cues; Data; Development; Disease; Electrophysiology (science); Engineering; Environment; Error Sources; Feedback; Fire - disasters; Hippocampus (Brain); Individual; Injury; Investigation; Location; Maps; Mathematics; Medial; Mental Health; Mental disorders; Modeling; Motion; Movement; Neuronal Plasticity; Neurons; Neurosciences; Positioning Attribute; Process; Rattus; Research; Research Personnel; Sensory; Signal Transduction; Site; Speed; System; Systems Integration; Systems Theory; Testing; Time; Update; base; brain circuitry; cell type; computer studies; control theory; dynamic system; entorhinal cortex; experience; experimental study; flexibility; insight; mathematical analysis; neurophysiology; novel; prevent; programs; relating to nervous system; response; sensory feedback; sensory input; theories; vector; visual control; visual feedback; way finding

The striking spatial correlates of hippocampal place cells and grid cells have provided unique insights into how the brain constructs internal, cognitive representations of the environment and uses these representations to guide behavior. These spatially selective cells are influenced by both self-motion signals and by external sensory landmarks. Self-motion signals provide the basis for a path integration computation, in which the hippocampal system tracks the animal's location by integrating its movement vector (speed and direction) over time to continuously update a position signal on an internal "cognitive map." To prevent accumulation of error, it is crucial that this endogenous spatial representation be anchored by stable, external sensory cues, such as individual landmarks and environmental boundaries. Accurate path integration requires that an internal representation of position be updated in precise agreement with the animal's displacement in the world. What if the relation between position calculated by self-motion cues and position defined by landmark cues is altered, e.g. during development (slow time scale) or due to injury (fast time scale)? Does the animal recalibrate the internal gain between representations of its movement and the updating of the representation of its position in the brain? We hypothesize that this gain must be learned by reference to visual feedback. We constructed an augmented reality system that allows precise, closed-loop control of the visual environment as rats move through physical space and provide evidence that the path integration system can indeed be recalibrated. We propose a collaborative research program to investigate plasticity of the path integration gain at multiple neural levels using combined theoretical, engineering, and experimental approaches. We will combine mathematical analysis, biologically inspired attractor network theory, and principles derived from engineering to develop the first models of how the path integration system dynamically recalibrates itself in response to sensory feedback. We will perform recordings from the hippocampus and medial entorhinal cortex to provide data to constrain and test these models. The combined expertise of the Pl and Co­ Investigators in electrophysiological recordings of the hippocampal system, engineering, and mathematical neuroscience will propel the theory forward to explain the network dynamics and functional implications of this ethologically critical form of neural plasticity.

海马位置细胞和网格细胞的显著空间相关性为大脑如何构建环境的内部认知表征并使用这些表征来指导行为提供了独特的见解。这些空间选择性细胞受到自我运动信号和外部感觉标志的影响。自我运动信号为路径整合计算提供了基础，其中海马系统通过随时间整合其运动矢量（速度和方向）来跟踪动物的位置，以连续更新内部“认知地图”上的位置信号。“为了防止错误的积累，这种内源性空间表征必须由稳定的外部感官线索来锚定，例如个人地标和环境边界。 精确的路径整合要求位置的内部表示与动物在世界中的位移精确一致地更新。如果由自我运动线索计算的位置和由地标线索定义的位置之间的关系发生改变，例如在发育期间（慢时间尺度）或由于受伤（快时间尺度）？动物是否会在大脑中重新校准其运动表征与其位置表征更新之间的内部增益？我们假设这种增益必须通过参考视觉反馈来学习。我们构建了一个增强现实系统，当老鼠在物理空间中移动时，它可以精确地闭环控制视觉环境，并提供证据证明路径集成系统确实可以重新校准。我们提出了一个合作研究计划，研究可塑性的路径集成增益在多个神经水平，结合理论，工程和实验方法。我们将结合联合收割机的数学分析，生物启发吸引子网络理论，并从工程原理推导出的路径集成系统如何动态地重新校准自己，以响应感官反馈的第一个模型。我们将从海马和内侧内嗅皮层进行记录，以提供数据来约束和测试这些模型。在海马系统，工程和数学神经科学的电生理记录的PI和Co的研究人员的综合专业知识将推动理论向前解释这种行为学关键形式的神经可塑性的网络动力学和功能的影响。