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A Plane-Dependent Model of 3D Grid Cells for Representing Both 2D and 3D Spaces Under Various Navigation Modes
Frontiers in Computational Neuroscience ( IF 3.2 ) Pub Date : 2021-09-22 , DOI: 10.3389/fncom.2021.739515
Ziyi Gong 1, 2 , Fangwen Yu 1
Affiliation  

Grid cells are crucial in path integration and representation of the external world. The spikes of grid cells spatially form clusters called grid fields, which encode important information about allocentric positions. To decode the information, studying the spatial structures of grid fields is a key task for both experimenters and theorists. Experiments reveal that grid fields form hexagonal lattice during planar navigation, and are anisotropic beyond planar navigation. During volumetric navigation, they lose global order but possess local order. How grid cells form different field structures behind these different navigation modes remains an open theoretical question. However, to date, few models connect to the latest discoveries and explain the formation of various grid field structures. To fill in this gap, we propose an interpretive plane-dependent model of three-dimensional (3D) grid cells for representing both two-dimensional (2D) and 3D space. The model first evaluates motion with respect to planes, such as the planes animals stand on and the tangent planes of the motion manifold. Projection of the motion onto the planes leads to anisotropy, and error in the perception of planes degrades grid field regularity. A training-free recurrent neural network (RNN) then maps the processed motion information to grid fields. We verify that our model can generate regular and anisotropic grid fields, as well as grid fields with merely local order; our model is also compatible with mode switching. Furthermore, simulations predict that the degradation of grid field regularity is inversely proportional to the interval between two consecutive perceptions of planes. In conclusion, our model is one of the few pioneers that address grid field structures in a general case. Compared to the other pioneer models, our theory argues that the anisotropy and loss of global order result from the uncertain perception of planes rather than insufficient training.



中文翻译:

用于在各种导航模式下表示 2D 和 3D 空间的 3D 网格单元的平面相关模型

网格单元对于路径整合和外部世界的表示至关重要。网格单元的尖峰在空间上形成称为网格场的簇,它编码有关异心位置的重要信息。为了解码信息,研究网格场的空间结构是实验者和理论家的关键任务。实验表明,网格场在平面导航过程中形成六边形点阵,并且在平面导航之外是各向异性的。在体积导航期间,它们失去全局顺序但拥有局部顺序。在这些不同的导航模式背后,网格单元如何形成不同的场结构仍然是一个悬而未决的理论问题。然而,迄今为止,很少有模型连接到最新发现并解释各种网格场结构的形成。为了填补这个空白,我们提出了一种三维(3D)网格单元的解释性平面相关模型,用于表示二维(2D)和3D空间。该模型首先评估相对于平面的运动,例如动物站立的平面和运动流形的切平面。将运动投影到平面上会导致各向异性,平面感知的错误会降低网格场的规则性。然后,无需训练的循环神经网络 (RNN) 将处理后的运动信息映射到网格字段。我们验证了我们的模型可以生成规则和各向异性的网格场,以及仅具有局部顺序的网格场;我们的模型还兼容模式切换。此外,模拟预测,网格场规则性的退化与平面的两个连续感知之间的间隔成反比。总之,我们的模型是在一般情况下解决网格场结构的少数先驱之一。与其他先驱模型相比,我们的理论认为,各向异性和全局秩序的丧失是由于对平面的不确定感知而不是训练不足造成的。

更新日期:2021-09-22
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