Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.
Department of Biotechnology, Rajalakshmi Engineering College, Chennai, India.
Front Neural Circuits. 2019 Jan 14;12:120. doi: 10.3389/fncir.2018.00120. eCollection 2018.
Grid cells are a special class of spatial cells found in the medial entorhinal cortex (MEC) characterized by their strikingly regular hexagonal firing fields. This spatially periodic firing pattern is originally considered to be independent of the geometric properties of the environment. However, this notion was contested by examining the grid cell periodicity in environments with different polarity (Krupic et al., 2015) and in connected environments (Carpenter et al., 2015). Aforementioned experimental results demonstrated the dependence of grid cell activity on environmental geometry. Analysis of grid cell periodicity on practically infinite variations of environmental geometry imposes a limitation on the experimental study. Hence we analyze the dependence of grid cell periodicity on the environmental geometry purely from a computational point of view. We use a hierarchical oscillatory network model where velocity inputs are presented to a layer of Head Direction cells, outputs of which are projected to a Path Integration layer. The Lateral Anti-Hebbian Network (LAHN) is used to perform feature extraction from the Path Integration neurons thereby producing a spectrum of spatial cell responses. We simulated the model in five types of environmental geometries such as: (1) connected environments, (2) convex shapes, (3) concave shapes, (4) regular polygons with varying number of sides, and (5) transforming environment. Simulation results point to a greater function for grid cells than what was believed hitherto. Grid cells in the model encode not just the local position but also more global information like the shape of the environment. Furthermore, the model is able to capture the invariant attributes of the physical space ingrained in its LAHN layer, thereby revealing its ability to classify an environment using this information. The proposed model is interesting not only because it is able to capture the experimental results but, more importantly, it is able to make many important predictions on the effect of the environmental geometry on the grid cell periodicity and suggesting the possibility of grid cells encoding the invariant properties of an environment.
网格细胞是一种特殊的空间细胞，存在于内侧内嗅皮层（MEC）中，其特征是其惊人的规则六边形发射场。这种空间周期性的发射模式最初被认为是独立于环境的几何性质的。然而，通过检查具有不同极性的环境中的网格细胞周期性（Krupic 等人，2015 年）和连接环境中的网格细胞周期性（Carpenter 等人，2015 年），这个概念受到了挑战。上述实验结果表明，网格细胞活动依赖于环境几何形状。对环境几何形状的实际无限变化的网格细胞周期性分析对实验研究提出了限制。因此，我们纯粹从计算的角度分析了网格细胞周期性对环境几何形状的依赖。我们使用分层振荡网络模型，其中速度输入被呈现给头方向细胞层，其输出被投影到路径积分层。侧向反赫布网络（LAHN）用于从路径积分神经元中提取特征，从而产生空间细胞反应的频谱。我们在五种环境几何形状（例如）中模拟了模型：（1）连接的环境，（2）凸形状，（3）凹形状，（4）具有不同边数的规则多边形，以及（5）转换环境。模拟结果表明，网格细胞的功能比以前认为的更重要。模型中的网格细胞不仅编码局部位置，而且还编码更多的全局信息，例如环境的形状。此外，该模型能够捕获物理空间中固有的不变属性，并利用该信息对环境进行分类，从而揭示其能力。所提出的模型不仅有趣，因为它能够捕捉实验结果，而且更重要的是，它能够对环境几何形状对网格细胞周期性的影响做出许多重要预测，并暗示网格细胞编码环境不变属性的可能性。
