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On the Organization of Grid and Place Cells: Neural Denoising via Subspace Learning

机译：关于网格和位置单元的组织：通过子空间学习的神经去噪

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摘要

Place cells in the hippocampus (HC) are active when an animal visits a certain location (referred to as a place field) within an environment. Grid cells in the medial entorhinal cortex (MEC) respond at multiple locations, with firing fields that form a periodic and hexagonal tiling of the environment. The joint activity of grid and place cell populations, as a function of location, forms a neural code for space. In this article, we develop an understanding of the relationships between coding theoretically relevant properties of the combined activity of these populations and how these properties limit the robustness of this representation to noise-induced interference. These relationships are revisited by measuring the performances of biologically realizable algorithms implemented by networks of place and grid cell populations, as well as constraint neurons, which perform denoising operations. Contributions of this work include the investigation of coding theoretic limitations of the mammalian neural code for location and how communication between grid and place cell networks may improve the accuracy of each population's representation. Simulations demonstrate that denoising mechanisms analyzed here can significantly improve the fidelity of this neural representation of space. Furthermore, patterns observed in connectivity of each population of simulated cells predict that anti-Hebbian learning drives decreases in inter-HC-MEC connectivity along the dorsoventral axis.
机译：当动物访问环境中的某个位置（称为放置场）时，海马（HC）中的放置细胞处于活动状态。内侧内嗅皮层（MEC）中的网格单元在多个位置进行响应，其发射场形成环境的周期性和六边形拼贴。网格和位置细胞种群的联合活动，作为位置的函数，形成了空间的神经代码。在本文中，我们发展了对这些种群的合并活动的理论相关属性进行编码的关系以及这些属性如何限制此表示对噪声引起的干扰的鲁棒性之间的关系的理解。通过测量由位置和网格单元种群网络以及执行降噪操作的约束神经元网络实现的可生物实现算法的性能，可以重新研究这些关系。这项工作的贡献包括研究哺乳动物神经代码的编码理论局限性，以及网格与位置细胞网络之间的通信如何提高每个群体表示的准确性。仿真表明，此处分析的去噪机制可以显着提高空间神经表示的保真度。此外，在每个模拟细胞群体的连通性中观察到的模式预测，沿背腹轴的HC-MEC间连通性会导致反希伯来学习驱动力下降。

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  • 来源
    《Neural computation》 |2019年第8期|1519-1550|共32页
  • 作者

    Schwartz David M.; Koyluoglu O. Ozan;

  • 作者单位

    Univ Arizona Dept Elect & Comp Engn Tucson AZ 85719 USA;

    Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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