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

基本信息

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
项目类别：
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

项目摘要

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着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信号的影响。自我运动信号为路径积分计算提供了基础，其中海马系统通过随着时间的推移积分其运动矢量（速度和方向）来跟踪动物的位置，以不断更新内部“认知”上的位置信号。地图。”为了防止误差累积，至关重要的是，这种内源性空间表征必须由稳定的外部感官线索（例如单个地标和环境边界）锚定。准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，如果在发育过程中（例如，慢时间尺度）由自运动线索计算的位置与由地标线索定义的位置之间的关系会怎样。）还是由于受伤（快速时间尺度）？动物是否会重新校准其运动表示和大脑中位置表示更新之间的内部增益？我们认为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允许在老鼠穿过物理空间时对视觉环境进行精确的闭环控制，并提供路径积分系统确实可以重新校准的证据，我们提出了一个合作研究计划来研究多个路径积分增益的可塑性。我们将结合数学分析、受生物学启发的吸引子网络理论和工程原理来开发路径集成系统如何动态地重新校准自身以响应感官反馈的第一个模型。将执行录音海马体和内侧内嗅皮层提供数据来约束和测试这些模型，PI 和 Co 研究人员在海马系统电生理记录、工程学和神经数学科学方面的综合专业知识将推动该理论向前发展，以解释网络动力学和功能含义。这种具有道德批判性的神经可塑性形式。