Yu Naigong, Liao Yishen, Zheng Xiangguo
Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Feb 25;37(1):27-37. doi: 10.7507/1001-5515.201901044.
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自身在空间中当前位置的主要依据。由于网格细胞是位置细胞的主要输入源，因此需要构建一个从网格细胞到位置细胞的映射模型。为解决这一问题，本文提出了一种从网格细胞到位置细胞的反向传播误差神经网络映射模型，该模型能够在给定区域内准确表达位置。根据边界细胞对环境特异性放电的生理特性，实现了边界细胞对网格场相位的周期性重置，并通过该模型完成了在任意空间中的位置识别。本文设计了仿真实验，比较了理论位置细胞层的活性，进而比较了竞争神经网络模型的耗时以及RatSLAM位姿细胞层的定位误差。实验结果表明，所提模型能够获得单一位置场，在耗时实验中算法效率比竞争神经网络模型提高了85.94%。在定位实验中，平均定位误差比RatSLAM位姿细胞层低41.35%。因此，本文提出的位置认知模型不仅能够实现网格细胞与位置细胞之间信息的高效传递，还能实现自身在任意空间区域内位置的精准定位。
