Department of Neurobiology, Stanford University, Stanford, CA 94305, USA.
Department of Neurobiology, Stanford University, Stanford, CA 94305, USA; Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.
Neuron. 2015 May 6;86(3):827-39. doi: 10.1016/j.neuron.2015.03.039. Epub 2015 Apr 16.
Medial entorhinal grid cells fire in periodic, hexagonally patterned locations and are proposed to support path-integration-based navigation. The recursive nature of path integration results in accumulating error and, without a corrective mechanism, a breakdown in the calculation of location. The observed long-term stability of grid patterns necessitates that the system either performs highly precise internal path integration or implements an external landmark-based error correction mechanism. To distinguish these possibilities, we examined grid cells in behaving rodents as they made long trajectories across an open arena. We found that error accumulates relative to time and distance traveled since the animal last encountered a boundary. This error reflects coherent drift in the grid pattern. Further, interactions with boundaries yield direction-dependent error correction, suggesting that border cells serve as a neural substrate for error correction. These observations, combined with simulations of an attractor network grid cell model, demonstrate that landmarks are crucial to grid stability.
内侧缰状回网格细胞呈周期性、六边形模式发射，并被提议支持基于路径整合的导航。路径整合的递归性质导致了误差的积累，如果没有纠正机制，位置计算就会崩溃。网格模式的长期稳定性需要系统要么进行高度精确的内部路径整合，要么实施基于外部地标物的误差纠正机制。为了区分这些可能性，我们在动物穿越开放竞技场的长轨迹中观察了行为啮齿动物的网格细胞。我们发现，误差是相对于动物上次遇到边界以来的时间和距离的积累。这种误差反映了网格模式的连贯漂移。此外，与边界的相互作用产生了与方向相关的误差修正，这表明边界细胞是用于错误修正的神经基质。这些观察结果，结合吸引子网络网格细胞模型的模拟，表明地标物对于网格稳定性至关重要。
