Computational Neuroscience Lab, Indian Institute of Technology Madras, Chennai, India.
Department of Electronics Engineering, Indian Institute of Technology (BHU), Varanasi, India.
Hippocampus. 2022 Oct;32(10):716-730. doi: 10.1002/hipo.23461. Epub 2022 Aug 10.
A special class of neurons in the hippocampal formation broadly known as the spatial cells, whose subcategories include place cells, grid cells, and head direction cells, are considered to be the building blocks of the brain's map of the spatial world. We present a general, deep learning-based modeling framework that describes the emergence of the spatial-cell responses and can also explain responses that involve a combination of path integration and vision. The first layer of the model consists of head direction (HD) cells that code for the preferred direction of the agent. The second layer is the path integration (PI) layer with oscillatory neurons: displacement of the agent in a given direction modulates the frequency of these oscillators. Principal component analysis (PCA) of the PI-cell responses showed the emergence of cells with grid-like spatial periodicity. We show that the Bessel functions could describe the response of these cells. The output of the PI layer is used to train a stack of autoencoders. Neurons of both the layers exhibit responses resembling grid cells and place cells. The paper concludes by suggesting the wider applicability of the proposed modeling framework beyond the two simulated studies.
海马结构中一类特殊的神经元被广泛称为空间细胞，其亚类包括位置细胞、网格细胞和头部方向细胞，被认为是大脑空间世界图谱的构建模块。我们提出了一个通用的基于深度学习的建模框架，该框架描述了空间细胞反应的出现，也可以解释涉及路径积分和视觉组合的反应。模型的第一层由头部方向 (HD) 细胞组成，这些细胞对代理的首选方向进行编码。第二层是具有振荡神经元的路径积分 (PI) 层：代理在给定方向上的位移会调节这些振荡器的频率。对 PI 细胞反应的主成分分析 (PCA) 显示出具有网格状空间周期性的细胞的出现。我们表明，贝塞尔函数可以描述这些细胞的响应。PI 层的输出用于训练堆叠的自动编码器。两个层的神经元都表现出类似于网格细胞和位置细胞的反应。本文最后提出，所提出的建模框架除了在这两个模拟研究之外，还有更广泛的适用性。
