Biological studies show that place cells are the main basis for rats to know their current location in space. Since grid cells are the main input source of place cells, a mapping model from grid cells to place cells needs to be constructed. To solve this problem, a neural network mapping model of back propagation error from grid cells to place cells is proposed in this paper, which can accurately express the location in a given region. According to the physiological characteristics of border cells' specific discharge to the environment, the periodic resetting of the grid field phase by border cells is realized, and the position recognition in any space is completed by this model. In this paper, we designed a simulation experiment to compare the activity of the theoretical place cell plate, and then compared the time consumption of the competitive neural network model and the positioning error of RatSLAM pose cells plate. The experimental results showed that the proposed model could obtain a single place field, and the algorithm efficiency was improved by 85.94% compared with the competitive neural network model in the time-consuming experiment. In the localization experiment, the mean localization error was 41.35% lower than that of RatSLAM pose cells plate. Therefore, the location cognition model proposed in this paper can not only realize the efficient transfer of information between grid cells and place cells, but also realize the accurate location of its own location in any spatial area.
生物学研究表明，位置细胞是大鼠知晓当前所处空间位置的主要依据。由于网格细胞是位置细胞的主要信息输入源，因此需要构建由网格细胞到位置细胞的映射模型。针对这一问题，本文提出一种网格细胞到位置细胞的逆传播误差神经网络映射模型，实现在给定区域内对位置的精确表达。又依据边界细胞对环境边界特异性放电这一生理特性，实现利用边界细胞对网格野位相的周期性重置，使该模型完成任意大小空间中的位置认知。本文设计了仿真实验对比理论位置细胞板的活动情况，又分别对比竞争型神经网络模型的耗时和 RatSLAM 位姿细胞板的定位误差。实验结果表明，本文模型能够得到单一的位置野，并在耗时实验中较竞争型神经网络模型算法效率提高 85.94%；在定位实验中较 RatSLAM 位姿细胞板的平均定位误差下降 41.35%。因此本文提出的位置认知模型不仅可以实现网格细胞到位置细胞之间信息的高效传递，而且能够在任意大小的空间区域内实现自身位置的精确定位。.
Keywords: border cells; grid cells; location cognition; place cells.