发表机构
University of Cincinnati（辛辛那提大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将网格细胞模块与BVC驱动的位置细胞结合，可在三类环境中使空间混叠减少94%-99%，证明网格细胞能为边界输入提供补充信息，提升几何模糊环境中位置表征的可靠性。
AI 中文摘要
空间混叠是指当两个或多个不同位置产生高度相似的位置细胞表征时出现的现象，主要由环境对称性或重复结构导致。当仅从边界向量细胞（BVC）输入构建位置表征时，该问题最为明显，因为对称或重复结构会在环境中的多个位置产生难以区分的感官模式。本研究引入网格细胞信号以缓解此类场景中的空间混叠。由于网格细胞会产生与环境几何无关的周期性、内部生成的空间信号，它们在区分感知上相同的位置方面发挥关键作用。我们将多个经分析构建的网格细胞模块与BVC驱动的位置细胞相结合，结果显示，相较于仅使用BVC的基线，在三种环境中空间混叠减少了94%至99%：无障碍物的开放环境、带有十字形中心障碍物（产生高视觉对称性）的环境以及迷宫环境。在视觉对称性最高的环境中，改善最为显著。这些结果表明，网格细胞提供了基于边界输入的补充信息，在几何模糊的环境中能产生更可靠的位置表征。
英文摘要
Spatial aliasing occurs when two or more distinct locations produce highly similar place-cell representations, primarily due to environmental symmetry or repetitive structures. This issue is most pronounced when place representations are constructed solely from boundary vector cell (BVC) inputs, because symmetric or repetitive structures can yield indistinguishable sensory patterns across multiple locations in an environment. This work introduces grid cell signals to mitigate spatial aliasing in such settings. Because grid cells contribute periodic, internally generated spatial signals that vary independently of environmental geometry, they play a key role in disambiguating perceptually identical locations. We integrate multiple modules of analytically constructed grid cells with BVC-driven place cells and show that this leads to a 94--99% reduction in spatial aliasing relative to a BVC-only baseline across three environments: an open environment without obstacles; an environment with a cross-shaped central obstacle creating high visual symmetry; and a maze environment. The greatest improvement occurs in the environment with the highest visual symmetry. These results indicate that grid cells provide information complementary to boundary-based inputs, yielding more reliable place representations in geometrically ambiguous environments.
CommentsIEEE World Congress on Computational Intelligence, Masstricht, Netherlands, June 2026
