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: 9900870
• 负责人: Noah John Cowan
• 金额: $ 35.74万
• 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 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9900870
• 关键词: Address; Affect; Agreement; Animals; Augmented Reality; Behavior; Biological; 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惊人空间相关性为大脑如何构建环境内部的认知表示形式提供了独特的见解，并使用这些表示形式来指导行为。这些空间选择性的细胞都受到自我运动信号和外部感觉地标的影响。自我运动信号为路径集成计算提供了基础，在该计算中，海马系统随着时间的推移将动物的移动向量（速度和方向）整合在一起，以不断更新内部“认知图”上的位置信号来跟踪动物的位置。为了防止误差的积累，至关重要的是，这种内源空间表示由稳定的外部感觉提示（例如个体地标和环境边界）锚定。 准确的路径集成要求将位置的内部表示与世界动物在世界上的位移的精确协议中进行更新。如果通过自我运动提示和地标线索定义的位置计算的位置之间的关系发生了变化，例如在开发期间（时间尺度缓慢）或由于受伤（快速时间尺度）？动物是否会重新校准其运动的表示和更新其在大脑中的位置的更新之间的内部增益？我们假设必须通过参考视觉反馈来学习这一收益。我们构建了一个增强的现实系统，该系统允许在大鼠穿越物理空间时精确，闭环控制视觉环境，并提供了可以开发路径积分系统的证据。我们提出了一项协作研究计划，以使用合并的理论，工程和实验方法来研究多个神经元水平的路径积分增益的可塑性。我们将结合数学分析，生物学启发的吸引者网络理论以及工程学得出的原理，以开发道路积分系统如何动态重新校准自身对感觉反馈的第一个模型。我们将从海马和内侧内嗅皮层中执行记录，以提供数据以约束和测试这些模型。 PL和CO研究者在海马系统，工程和数学神经科学的电生理记录中的专业知识将推动该理论的前进，以解释这种神经塑性的伦理上关键形式的网络动态和功能含义。