A spatial cognition model based on the selection mechanism of hippocampus place cells_Journal of Biomedical Engineering

Authors：

 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;

Corresponding author：

YU Naigong, Email: yunaigong@bjut.edu.cn

Keywords：

place cells; grid cells; border cells; location cognition

DOI：

10.7507/1001-5515.201901044

Video：

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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.

Citation： YU Naigong, LIAO Yishen, ZHENG Xiangguo. A spatial cognition model based on the selection mechanism of hippocampus place cells. Journal of Biomedical Engineering, 2020, 37(1): 27-37. doi: 10.7507/1001-5515.201901044 Copy

1.	O’Keefe J, Dostrovsky J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res, 1971, 34(1): 171-175.
2.	O’Keefe J. Place units in the hippocampus of the freely moving rat. Exp Neurol, 1976, 51(1): 78-109.
3.	Moser E I, Kropff E, Moser M B. Place cells, grid cells, and the brain’s spatial representation system. Annu Rev Neurosci, 2008, 31(1): 69-89.
4.	Jeffery K J, Burgess N. A metric for the cognitive map: found at last? Trends Cogn Sci, 2006, 10(1): 1-3.
5.	Hafting T, Fyhn M, Molden S, et al. Microstructure of a spatial map in the entorhinal cortex. Nature, 2005, 436(752): 801-806.
6.	O’Keefe J, Burgess N. Geometric determinants of the place fields of hippocampal neurons. Nature, 1996, 381(6581): 425-428.
7.	Solstad T, Boccara C N, Kropff E, et al. Representation of geometric borders in the entorhinal cortex. Science, 2008, 322(599): 1865-1868.
8.	Savelli F, Yoganarasimha D, Knierim J J. Influence of boundary removal on the spatial representations of the medial entorhinal cortex. Hippocampus, 2008, 18(12, SI): 1270-1282.
9.	于乃功, 方略, 罗子维, 等. 一种网格细胞到位置细胞的高斯分布激活函数模型. 生物医学工程学杂志, 2016, 33(6): 1158-1167.
10.	于乃功, 王琳, 李倜, 等. 网格细胞到位置细胞的竞争型神经网络模型. 控制与决策, 2015, 30(8): 1372-1378.
11.	Franzius M, Vollgraf R, Wiskott L. From grids to places. J Comput Neurosci, 2007, 22(3): 297-299.
12.	周阳, 吴德伟. 基于网格细胞到位置细胞转换的位置估计模型. 电子与信息学报, 2017, 39(9): 2272-2276.
13.	于乃功, 苑云鹤, 李倜, 等. 一种基于海马认知机理的仿生机器人认知地图构建方法. 自动化学报, 2018, 44(1): 52-73.
14.	Milford M J, Wyeth G F, Prasser D. RatSLAM: a hippocampal model for simultaneous localization and mapping// 2004 IEEE International Conference on Robotics and Automation, 2004. New Orleans: IEEE, 2004, 1: 403-408.0.
15.	Mhatre H, Gorchetchnikov A, Grossberg S. Grid cell hexagonal patterns formed by fast self-organized learning within entorhinal cortex. Hippocampus, 2012, 22(2): 320-334.
16.	Castro L, Aguiar P. A feedforward model for the formation of a grid field where spatial information is provided solely from place cells. Biol Cybern, 2014, 108(2): 133-143.
17.	Tocker G, Barak O, Derdikman D. Grid cells correlation structure suggests organized feedforward projections into superficial layers of the medial entorhinal cortex. Hippocampus, 2015, 25(12): 1599-1613.
18.	于乃功, 方略, 罗子维, 等. 大鼠脑海马结构认知机理及其在机器人导航中的应用. 北京工业大学学报, 2017, 43(3): 434-442.
19.	Hardcastle K, Ganguli S, Giocomo L M. Environmental boundaries as an error correction mechanism for grid cells. Neuron, 2015, 86(3): 827-839.

1. O’Keefe J, Dostrovsky J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res, 1971, 34(1): 171-175.
2. O’Keefe J. Place units in the hippocampus of the freely moving rat. Exp Neurol, 1976, 51(1): 78-109.
3. Moser E I, Kropff E, Moser M B. Place cells, grid cells, and the brain’s spatial representation system. Annu Rev Neurosci, 2008, 31(1): 69-89.
4. Jeffery K J, Burgess N. A metric for the cognitive map: found at last? Trends Cogn Sci, 2006, 10(1): 1-3.
5. Hafting T, Fyhn M, Molden S, et al. Microstructure of a spatial map in the entorhinal cortex. Nature, 2005, 436(752): 801-806.
6. O’Keefe J, Burgess N. Geometric determinants of the place fields of hippocampal neurons. Nature, 1996, 381(6581): 425-428.
7. Solstad T, Boccara C N, Kropff E, et al. Representation of geometric borders in the entorhinal cortex. Science, 2008, 322(599): 1865-1868.
8. Savelli F, Yoganarasimha D, Knierim J J. Influence of boundary removal on the spatial representations of the medial entorhinal cortex. Hippocampus, 2008, 18(12, SI): 1270-1282.
9. 于乃功, 方略, 罗子维, 等. 一种网格细胞到位置细胞的高斯分布激活函数模型. 生物医学工程学杂志, 2016, 33(6): 1158-1167.
10. 于乃功, 王琳, 李倜, 等. 网格细胞到位置细胞的竞争型神经网络模型. 控制与决策, 2015, 30(8): 1372-1378.
11. Franzius M, Vollgraf R, Wiskott L. From grids to places. J Comput Neurosci, 2007, 22(3): 297-299.
12. 周阳, 吴德伟. 基于网格细胞到位置细胞转换的位置估计模型. 电子与信息学报, 2017, 39(9): 2272-2276.
13. 于乃功, 苑云鹤, 李倜, 等. 一种基于海马认知机理的仿生机器人认知地图构建方法. 自动化学报, 2018, 44(1): 52-73.
14. Milford M J, Wyeth G F, Prasser D. RatSLAM: a hippocampal model for simultaneous localization and mapping// 2004 IEEE International Conference on Robotics and Automation, 2004. New Orleans: IEEE, 2004, 1: 403-408.0.
15. Mhatre H, Gorchetchnikov A, Grossberg S. Grid cell hexagonal patterns formed by fast self-organized learning within entorhinal cortex. Hippocampus, 2012, 22(2): 320-334.
16. Castro L, Aguiar P. A feedforward model for the formation of a grid field where spatial information is provided solely from place cells. Biol Cybern, 2014, 108(2): 133-143.
17. Tocker G, Barak O, Derdikman D. Grid cells correlation structure suggests organized feedforward projections into superficial layers of the medial entorhinal cortex. Hippocampus, 2015, 25(12): 1599-1613.
18. 于乃功, 方略, 罗子维, 等. 大鼠脑海马结构认知机理及其在机器人导航中的应用. 北京工业大学学报, 2017, 43(3): 434-442.
19. Hardcastle K, Ganguli S, Giocomo L M. Environmental boundaries as an error correction mechanism for grid cells. Neuron, 2015, 86(3): 827-839.

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