A spatial localization model of mobile robot based on entorhinal-hippocampal cognitive mechanism in rat brain_Journal of Biomedical Engineering

Authors：

 YU Naigong , LIAO Yishen

Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, P. R. China;

Corresponding author：

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

Keywords：

Place cells; Grid cells; Entorhinal-hippocampal; Spatial localization; Path integration

DOI：

10.7507/1001-5515.202109051

Video：

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Physiological studies reveal that rats rely on multiple spatial cells for spatial navigation and memory. In this paper, we investigated the firing mechanism of spatial cells within the entorhinal-hippocampal structure of the rat brain and proposed a spatial localization model for mobile robot. Its characteristics were as follows: on the basis of the information transmission model from grid cells to place cells, the neural network model of place cells interaction was introduced to obtain the place cell plate with a single-peaked excitatory activity package. Then the solution to the robot’s position was achieved by establishing a transformation relationship between the position of the excitatory activity package on the place cell plate and the robot’s position in the physical environment. In this paper, simulation experiments and physical experiments were designed to verify the model. The experimental results showed that compared with RatSLAM and the model of grid cells to place cells, the positioning performance of the model in this paper was more accurate, and the cumulative error in the long-time path integration process of the robot was also smaller. The research results of this paper lay a foundation for the robot navigation method that mimics the cognitive mechanism of rat brain.

Citation： YU Naigong, LIAO Yishen. A spatial localization model of mobile robot based on entorhinal-hippocampal cognitive mechanism in rat brain. Journal of Biomedical Engineering, 2022, 39(2): 217-227. doi: 10.7507/1001-5515.202109051 Copy

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2.	Burak Y, Fiete I R, Sporns O. Accurate path integration in continuous attractor network models of grid cells. PLoS Comput Biol, 2008, 5(2): e1000291.
3.	Bolding K A, Ferbinteanu J, Fox S E, et al. Place cell firing cannot support navigation without intact septal circuits. Hippocampus, 2020, 30(3): 175-191.
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5.	Wagatsuma H, Yamaguchi Y. Neural dynamics of the cognitive map in the hippocampus. Cogn Neurodyn, 2007, 1(2): 119-141.
6.	Li T Y, Arleo A, Sheynikhovich D. Modeling place cells and grid cells in multi-compartment environments: Entorhinal–hippocampal loop as a multisensory integration circuit. Neural Netw, 2020, 121: 37-51.
7.	Taube J S, Muller R U, Ranck J B. Head-direction cells recorded from the postsubiculum in freely moving rats. J Neurosci, 1990, 10(2): 420-435.
8.	Muller R U, Ranck J B, Taube J S. Head direction cells: properties and functional significance. Curr Opin Neurobiol, 1996, 6(2): 196-206.
9.	Kropff E, Carmichael J E, Moser M B, et al. Speed cells in the medial entorhinal cortex. Nature, 2015, 523(7561): 419-424.
10.	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.
11.	Krupic J, Burgess N, O’Keefe J. Neural representations of location composed of spatially periodic bands. Science, 2012, 337(6096): 853-857.
12.	McNaughton B L, Battaglia F P, Jensen O, et al. Path integration and the neural basis of the ‘cognitive map’. Nat Rev Neurosci, 2006, 7(8): 663-678.
13.	Moser E I, Roudi Y, Witter M P, et al. Grid cells and cortical representation. Nat Rev Neurosci, 2014, 15(7): 466-481.
14.	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.
15.	Wang Y H, Xu X Y, Pan X C, et al. Grid cell activity and path integration on 2-D manifolds in 3-D space. Nonlinear Dyn, 2021, 104(2): 1767-1780.
16.	Burgess N, Barry C, O’Keefe J. An oscillatory interference model of grid cell firing. Hippocampus, 2010, 17(9): 801-812.
17.	Erdem U M, Hasselmo M. A goal-directed spatial navigation model using forward trajectory planning based on grid cells. Eur J Neurosci, 2012, 35(6): 916-931.
18.	Si B L, Romani S, Tsodyks M, et al. Continuous attractor network model for conjunctive position-by-velocity tuning of grid cells. PLoS Comput Biol, 2014, 10(4): e1003558.
19.	Stepanyuk A. Self-organization of grid fields under supervision of place cells in the model of neuron with associative plasticity. Biol Inspired Cogn Archit, 2015, 13: 48-62.
20.	Kang L, Balasubramanian V. A geometric attractor mechanism for self-organization of entorhinal grid modules. eLife, 2019, 8: e46687.
21.	Cheng S, Frank L M. The structure of networks that produce the transformation from grid cells to place cells. Neuroscience, 2011, 197: 293-306.
22.	Solstad T, Moser E I, Einevoll G T. From grid cells to place cells: A mathematical model. Hippocampus, 2006, 16(12): 1026-1031.
23.	Bush D, Barry C, Burgess N. What do grid cells contribute to place cell firing?. Trends Neurosci, 2014, 37(3): 136-145.
24.	于乃功, 廖诣深, 郑相国. 一种基于海马位置细胞选择机制的空间认知模型. 生物医学工程学杂志, 2020, 37(1): 27-37.
25.	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.
26.	Rolls E T, Stringer S M, Elliot T. Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning. Network Comp Neural, 2006, 17(4): 447-465.
27.	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.
28.	Milford M J, Wyeth G F. Mapping a suburb with a single camera using a biologically inspired SLAM system. IEEE Trans Robot, 2008, 24(5): 1038-1053.
29.	Ball D, Heath S, Wiles J, et al. OpenRatSLAM: an open source brain-based SLAM system. Auton Robot, 2013, 34(3): 149-176.
30.	Yu F W, Shang J G, Hu Y J, et al. NeuroSLAM: a brain-inspired slam system for 3D environments. Biol Cybern, 2019, 113(5-6): 515-545.
31.	Zou Q, Cong M, Liu D, et al. Robotic episodic cognitive learning inspired by hippocampal spatial cells. IEEE Robot Autom Lett, 2020, 5(4): 5573-5580.
32.	李伟龙, 吴德伟, 卢虎, 等. 一种阈值动态调整的仿生同步自主定位方法. 西安交通大学学报, 2017, 51(10): 100-106.
33.	Rumelhart D E, Hinton G E, Williams R J. Learning representations by back propagating errors. Nature, 1986, 323(6088): 533-536.
34.	Hafting T, Fyhn M, Molden S, et al. Microstructure of a spatial map in the entorhinal cortex. Nature, 2005, 436(7052): 801-806.

1. Moser M B, Colgin L L, Igarashi K M, et al. Coordination of entorhinal-hippocampal ensemble activity during associative learning. Nature, 2014, 510(7503): 143-147.
2. Burak Y, Fiete I R, Sporns O. Accurate path integration in continuous attractor network models of grid cells. PLoS Comput Biol, 2008, 5(2): e1000291.
3. Bolding K A, Ferbinteanu J, Fox S E, et al. Place cell firing cannot support navigation without intact septal circuits. Hippocampus, 2020, 30(3): 175-191.
4. 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.
5. Wagatsuma H, Yamaguchi Y. Neural dynamics of the cognitive map in the hippocampus. Cogn Neurodyn, 2007, 1(2): 119-141.
6. Li T Y, Arleo A, Sheynikhovich D. Modeling place cells and grid cells in multi-compartment environments: Entorhinal–hippocampal loop as a multisensory integration circuit. Neural Netw, 2020, 121: 37-51.
7. Taube J S, Muller R U, Ranck J B. Head-direction cells recorded from the postsubiculum in freely moving rats. J Neurosci, 1990, 10(2): 420-435.
8. Muller R U, Ranck J B, Taube J S. Head direction cells: properties and functional significance. Curr Opin Neurobiol, 1996, 6(2): 196-206.
9. Kropff E, Carmichael J E, Moser M B, et al. Speed cells in the medial entorhinal cortex. Nature, 2015, 523(7561): 419-424.
10. 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.
11. Krupic J, Burgess N, O’Keefe J. Neural representations of location composed of spatially periodic bands. Science, 2012, 337(6096): 853-857.
12. McNaughton B L, Battaglia F P, Jensen O, et al. Path integration and the neural basis of the ‘cognitive map’. Nat Rev Neurosci, 2006, 7(8): 663-678.
13. Moser E I, Roudi Y, Witter M P, et al. Grid cells and cortical representation. Nat Rev Neurosci, 2014, 15(7): 466-481.
14. 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.
15. Wang Y H, Xu X Y, Pan X C, et al. Grid cell activity and path integration on 2-D manifolds in 3-D space. Nonlinear Dyn, 2021, 104(2): 1767-1780.
16. Burgess N, Barry C, O’Keefe J. An oscillatory interference model of grid cell firing. Hippocampus, 2010, 17(9): 801-812.
17. Erdem U M, Hasselmo M. A goal-directed spatial navigation model using forward trajectory planning based on grid cells. Eur J Neurosci, 2012, 35(6): 916-931.
18. Si B L, Romani S, Tsodyks M, et al. Continuous attractor network model for conjunctive position-by-velocity tuning of grid cells. PLoS Comput Biol, 2014, 10(4): e1003558.
19. Stepanyuk A. Self-organization of grid fields under supervision of place cells in the model of neuron with associative plasticity. Biol Inspired Cogn Archit, 2015, 13: 48-62.
20. Kang L, Balasubramanian V. A geometric attractor mechanism for self-organization of entorhinal grid modules. eLife, 2019, 8: e46687.
21. Cheng S, Frank L M. The structure of networks that produce the transformation from grid cells to place cells. Neuroscience, 2011, 197: 293-306.
22. Solstad T, Moser E I, Einevoll G T. From grid cells to place cells: A mathematical model. Hippocampus, 2006, 16(12): 1026-1031.
23. Bush D, Barry C, Burgess N. What do grid cells contribute to place cell firing?. Trends Neurosci, 2014, 37(3): 136-145.
24. 于乃功, 廖诣深, 郑相国. 一种基于海马位置细胞选择机制的空间认知模型. 生物医学工程学杂志, 2020, 37(1): 27-37.
25. 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.
26. Rolls E T, Stringer S M, Elliot T. Entorhinal cortex grid cells can map to hippocampal place cells by competitive learning. Network Comp Neural, 2006, 17(4): 447-465.
27. 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.
28. Milford M J, Wyeth G F. Mapping a suburb with a single camera using a biologically inspired SLAM system. IEEE Trans Robot, 2008, 24(5): 1038-1053.
29. Ball D, Heath S, Wiles J, et al. OpenRatSLAM: an open source brain-based SLAM system. Auton Robot, 2013, 34(3): 149-176.
30. Yu F W, Shang J G, Hu Y J, et al. NeuroSLAM: a brain-inspired slam system for 3D environments. Biol Cybern, 2019, 113(5-6): 515-545.
31. Zou Q, Cong M, Liu D, et al. Robotic episodic cognitive learning inspired by hippocampal spatial cells. IEEE Robot Autom Lett, 2020, 5(4): 5573-5580.
32. 李伟龙, 吴德伟, 卢虎, 等. 一种阈值动态调整的仿生同步自主定位方法. 西安交通大学学报, 2017, 51(10): 100-106.
33. Rumelhart D E, Hinton G E, Williams R J. Learning representations by back propagating errors. Nature, 1986, 323(6088): 533-536.
34. Hafting T, Fyhn M, Molden S, et al. Microstructure of a spatial map in the entorhinal cortex. Nature, 2005, 436(7052): 801-806.

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