Grid cell modules coordination improves accuracy and reliability for spatial navigation.

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES
Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-05-19 DOI:10.1007/s11571-025-10263-9
Luca Sarramone, Jose A Fernandez-Leon
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引用次数: 0

Abstract

Most mammals efficiently overcome self-localization deviations by coordinating grid and place cells in their brain's navigation system. However, the coordination of grid cell modules during spatial navigation and its impact on position estimation are poorly understood. This study addresses this issue by introducing a system that decodes grid-cell module activity and integrates networks of multiple grid-cell modules for self-position estimation in a mobile robot. Our results show that even when individual grid module estimates deviated substantially from the robot's actual location, the modules remained tightly coordinated. Corrections of these deviations were studied based on anchoring the activity of grid cells to spatial landmarks. Detailed numerical investigations indicate that path integration is critically dependent on the intrinsic coordination between grid cell modules which enhances the accuracy and reliability of spatial navigation. Furthermore, we show that this coordination enables effective vector navigation, even when the overall position estimation is inaccurate. These insights advance our understanding of grid-cell module coordination in location estimation during path integration and offer potential applications in robotics.

网格单元模块的协调提高了空间导航的精度和可靠性。
大多数哺乳动物通过协调大脑导航系统中的网格和位置细胞,有效地克服了自我定位偏差。然而,在空间导航过程中,网格单元模块的协调及其对位置估计的影响却知之甚少。本研究通过引入一个系统来解决这个问题,该系统可以解码网格单元模块的活动,并集成多个网格单元模块的网络,用于移动机器人的自我位置估计。我们的结果表明,即使单个网格模块的估计与机器人的实际位置有很大的偏差,模块仍然保持紧密协调。基于将网格细胞的活动锚定到空间地标,研究了这些偏差的修正。详细的数值研究表明,路径积分主要依赖于网格单元之间的内在协调,从而提高了空间导航的精度和可靠性。此外,我们表明,即使在整体位置估计不准确的情况下,这种协调也可以实现有效的矢量导航。这些见解促进了我们对路径集成过程中位置估计中网格单元模块协调的理解,并为机器人技术提供了潜在的应用。
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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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