Place cells and geometry lead to a flexible grid pattern.

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2021-11-01 Epub Date: 2021-06-14 DOI:10.1007/s10827-021-00794-5
Wenjing Wang, Wenxu Wang
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引用次数: 2

Abstract

Place cells and grid cells are important neurons involved in spatial navigation in the mammalian brain. Grid cells are believed to play an important role in forming a cognitive map of the environment. Experimental observations in recent years showed that the grid pattern is not invariant but is influenced by the shape of the spatial environment. However, the cause of this deformation remains elusive. Here, we focused on the functional interactions between place cells and grid cells, utilizing the information of location relationships between the firing fields of place cells to optimize the previous grid cell feedforward generation model and expand its application to more complex environmental scenarios. Not only was the regular equilateral triangle periodic firing field structure of the grid cells reproduced, but the expected results were consistent with the experiment for the environment with various complex boundary shapes and environmental deformation. Even in the field of three-dimensional spatial grid patterns, forward-looking predictions have been made. This provides a possible model explanation for how the coupling of grid cells and place cells adapt to the diversity of the external environment to deepen our understanding of the neural basis for constructing cognitive maps.

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位置单元和几何结构形成灵活的网格模式。
位置细胞和网格细胞是哺乳动物大脑中参与空间导航的重要神经元。网格细胞被认为在形成环境的认知地图中起着重要作用。近年来的实验观测表明,网格模式不是不变的,而是受空间环境形状的影响。然而,这种变形的原因仍然难以捉摸。本文重点研究了位置细胞与网格细胞之间的功能相互作用,利用位置细胞放电场之间的位置关系信息,对网格细胞前馈生成模型进行了优化,并将其应用于更复杂的环境场景。不仅再现了网格细胞的正等边三角形周期放电场结构,而且在各种复杂边界形状和环境变形的环境下,其预期结果与实验结果一致。即使在三维空间网格模式领域,也做出了前瞻性的预测。这为网格细胞和位置细胞的耦合如何适应外部环境的多样性提供了一个可能的模型解释,从而加深了我们对构建认知地图的神经基础的理解。
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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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