基于边界单元的网格单元动态自组织纠错
Dynamic self-organized error-correction of grid cells by border cells
摘要：
内嗅皮层中的网格细胞被认为是通过动物运动的路径整合来建立其规则的、空间相关的放电模式。路径积分的机制，例如在吸引子网络模型中，预测网格响应的随机漂移，这在实验中没有观察到。我们展示了一种生物学上合理的动态自组织机制，通过这种机制，在环境边界上开火的边界细胞可以纠正网格细胞中的这种漂移。在我们的模型中，探索过程中的经验依赖性赫布可塑性允许边界细胞学习与网格细胞的连接。这个学习网络中的边界细胞重置漂移网格的相位。这种纠错机制对环境形状和复杂性（包括具有内部屏障的外壳）具有鲁棒性，并对环境变形实验进行了独特的预测。我们的工作证明了内嗅皮层中不同类型的细胞如何动态地、自适应地相互作用，以实现强大的路径整合。
英文摘要：
Grid cells in the entorhinal cortex are believed to establish their regular, spatially correlated firing patterns by path integration of the animal's motion. Mechanisms for path integration, e.g. in attractor network models, predict stochastic drift of grid responses, which is not observed experimentally. We demonstrate a biologically plausible mechanism of dynamic self-organization by which border cells, which fire at environmental boundaries, can correct such drift in grid cells. In our model, experience-dependent Hebbian plasticity during exploration allows border cells to learn connectivity to grid cells. Border cells in this learned network reset the phase of drifting grids. This error-correction mechanism is robust to environmental shape and complexity, including enclosures with interior barriers, and makes distinctive predictions for environmental deformation experiments. Our work demonstrates how diverse cell types in the entorhinal cortex could interact dynamically and adaptively to achieve robust path integration.
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