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

海马区细胞和网格细胞的惊人空间关联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既受到自我运动信号的影响，也受到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，在路径整合计算中，海马体系统通过整合动物随时间的移动向量(速度和方向)来跟踪动物的位置，以不断更新内部“认知地图”上的位置信号。为了防止误差的累积，这种内生的空间表征必须以稳定的外部感官线索为基础，例如个别地标和环境边界。 精确的路径整合需要根据动物在世界上的位移精确地更新内部位置表示。如果在发育过程中(慢时间尺度)或由于受伤(快时间尺度)，由自我运动线索计算的位置与由标志性线索定义的位置之间的关系发生改变，该怎么办？动物是否会重新调整其运动表征和大脑中位置表征的更新之间的内在收益？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，可以对视觉环境进行精确的闭环控制，并提供证据表明路径整合系统确实可以重新校准。我们提出了一个合作研究计划，利用理论、工程和实验相结合的方法，在多个神经水平上研究路径整合增益的可塑性。我们将结合数学分析、生物启发的吸引子网络理论和来自工程的原理来开发路径整合系统如何动态地重新校准自己以响应感觉反馈的第一个模型。我们将从海马体和内侧嗅觉皮质进行记录，以提供数据来约束和测试这些模型。Pl和合作研究人员在海马体系统电生理记录、工程学和数学神经科学方面的综合专业知识将推动这一理论向前发展，以解释这种神经可塑性的行为学关键形式的网络动力学和功能含义。

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.