A grid field calculation model based on perceived speed and perceived angle_Journal of Biomedical Engineering

Authors：

 YU Naigong ^1,2 , FENG Hui ^1,2 , LIAO Yishen ^1,2 , ZHENG Xiangguo ^1,2

1. Department of Informatics, Beijing University of Technology, Beijing 100124, P.R.China;
2. 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：

perceived speed; perceived angle; speed cell; grid cell; grid field computing model

DOI：

10.7507/1001-5515.201911058

Video：

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Abstract Full text Figures/Tables Video References Cited by

The method of directly using speed information and angle information to drive attractors model of grid cells to encode environment has poor anti-interference ability and is not bionic. In response to the problem, this paper proposes a grid field calculation model based on perceived speed and perceived angle. The model has the following characteristics. Firstly, visual stream is decoded to obtain visual speed, and speed cell is modeled and decoded to obtain body speed. Visual speed and body speed are integrated to obtain perceived speed information. Secondly, a one-dimensional circularly connected cell model with excitatory connection is used to simulate the firing mechanism of head direction cells, so that the robot obtains current perception angle information in a biomimetic manner. Finally, the two kinds of perceptual information of speed and angle are combined to realize the driving of grid cell attractors model. The proposed model was experimentally verified. The results showed that this model could realize periodic hexagonal firing field mode of grid cells and precise path integration function. The proposed algorithm may provide a foundation for the research on construction method of robot cognitive map based on hippocampal cognition mechanism.

Citation： YU Naigong, FENG Hui, LIAO Yishen, ZHENG Xiangguo. A grid field calculation model based on perceived speed and perceived angle. Journal of Biomedical Engineering, 2020, 37(5): 863-874. doi: 10.7507/1001-5515.201911058 Copy

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2.	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.
3.	Taube J S. Head direction cells recorded in the anterior thalamic nuclei of freely moving rats. J Neurosci, 1995, 15(1): 70-86.
4.	Kropff E, Carmichael J E, Moser M B, et al. Speed cells in the medial entorhinal cortex. Nature, 2015, 523(7561): 419-424.
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.	Solstad T, Moser E I, Einevoll G T. From grid cells to place cells: A mathematical model. Hippocampus, 2006, 16(12): 1026-1031.
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8.	Burgess N, Barry C, O’Keefe J. An oscillatory interference model of grid cell firing. Hippocampus, 2007, 17(9): 801-812.
9.	Hasselmo M E, Giocomo L M, Zilli E A. Grid cell firing May arise from interference of theta frequency membrane potential oscillations in single neurons. Hippocampus, 2007, 17(12): 1252-1271.
10.	Si Bailu, Romani S, Tsodyks M. Continuous attractor network model for conjunctive position-by-velocity tuning of grid cells. PLoS Comput Biol, 2014, 10(4): e1003558.
11.	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.
12.	Fuhs M C, Touretzky D S. A spin glass model of path integration in rat medial entorhinal cortex. J Neurosci, 2006, 26(16): 4266-4276.
13.	Burak Y, Fiete I R. Accurate path integration in continuous attractor network models of grid cells. PLoS Comput Biol, 2009, 5(2): e1000291.
14.	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.
15.	Mittelstaedt M L, Mittelstaedt H. Homing by path integration in a mammal. Sci Nat, 1980, 67(11): 566-567.
16.	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.
17.	Yoder R M, Taube J S. The vestibular contribution to the head direction signal and navigation. Front Integr Neurosci, 2014, 8(4): 32-38.
18.	Raudies F, Hasselmo M E. Differences in visual-spatial input may underlie different compression properties of firing fields for grid cell modules in medial entorhinal cortex. PLoS Comput Biol, 2015, 11(11): e1004596.
19.	谢康宁. 被动运动相关的海马神经信号解析与信息编码模型研究. 北京: 清华大学, 2012.
20.	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.
21.	罗子维. 基于鼠脑空间认知机理的空间表达方法研究. 北京: 北京工业大学, 2018.
22.	Farnebck G. Two-frame motion estimation based on polynomial expansion. Image Anal, 2003, 2749: 363-370.
23.	崔海清, 刘希玉. 基于粒子群算法的 RBF 网络参数优化算法. 计算机技术与发展, 2009, 19(12): 117-119.
24.	李林, 李建兵, 牛鹏超. 基于粒子群算法的 RBF 神经网络的优化方法. 山东电力高等专科学校学报, 2010, 13(1): 51-53.
25.	Campbell M G, Ocko S A, Mallory C S, et al. Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation. Nat Neurosci, 2018, 21(8): 1096-1106.
26.	Zhang K. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory. J Neurosci, 1996, 16(6): 2112-2126.
27.	Paul A D, Emma R W, Anna S. A new perspective on the head direction cell system and spatial behavior. Neurosci Biobehav Rev, 2019, 105(2019): 24-33.
28.	Chen Guifen, Lu Yi, King J A, et al. Differential influences of environment and self-motion on place and grid cell firing. Nat Commun, 2019, 10(1): 630-640.
29.	李倜. 基于鼠脑海马认知机理的机器人面向目标的导航模型研究. 北京: 北京工业大学, 2016.

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. 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.
3. Taube J S. Head direction cells recorded in the anterior thalamic nuclei of freely moving rats. J Neurosci, 1995, 15(1): 70-86.
4. Kropff E, Carmichael J E, Moser M B, et al. Speed cells in the medial entorhinal cortex. Nature, 2015, 523(7561): 419-424.
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. Solstad T, Moser E I, Einevoll G T. From grid cells to place cells: A mathematical model. Hippocampus, 2006, 16(12): 1026-1031.
7. Moser E I, Moser M B. A metric for space. Hippocampus, 2008, 18(12): 1142-1156.
8. Burgess N, Barry C, O’Keefe J. An oscillatory interference model of grid cell firing. Hippocampus, 2007, 17(9): 801-812.
9. Hasselmo M E, Giocomo L M, Zilli E A. Grid cell firing May arise from interference of theta frequency membrane potential oscillations in single neurons. Hippocampus, 2007, 17(12): 1252-1271.
10. Si Bailu, Romani S, Tsodyks M. Continuous attractor network model for conjunctive position-by-velocity tuning of grid cells. PLoS Comput Biol, 2014, 10(4): e1003558.
11. 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.
12. Fuhs M C, Touretzky D S. A spin glass model of path integration in rat medial entorhinal cortex. J Neurosci, 2006, 26(16): 4266-4276.
13. Burak Y, Fiete I R. Accurate path integration in continuous attractor network models of grid cells. PLoS Comput Biol, 2009, 5(2): e1000291.
14. 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.
15. Mittelstaedt M L, Mittelstaedt H. Homing by path integration in a mammal. Sci Nat, 1980, 67(11): 566-567.
16. 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.
17. Yoder R M, Taube J S. The vestibular contribution to the head direction signal and navigation. Front Integr Neurosci, 2014, 8(4): 32-38.
18. Raudies F, Hasselmo M E. Differences in visual-spatial input may underlie different compression properties of firing fields for grid cell modules in medial entorhinal cortex. PLoS Comput Biol, 2015, 11(11): e1004596.
19. 谢康宁. 被动运动相关的海马神经信号解析与信息编码模型研究. 北京: 清华大学, 2012.
20. 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.
21. 罗子维. 基于鼠脑空间认知机理的空间表达方法研究. 北京: 北京工业大学, 2018.
22. Farnebck G. Two-frame motion estimation based on polynomial expansion. Image Anal, 2003, 2749: 363-370.
23. 崔海清, 刘希玉. 基于粒子群算法的 RBF 网络参数优化算法. 计算机技术与发展, 2009, 19(12): 117-119.
24. 李林, 李建兵, 牛鹏超. 基于粒子群算法的 RBF 神经网络的优化方法. 山东电力高等专科学校学报, 2010, 13(1): 51-53.
25. Campbell M G, Ocko S A, Mallory C S, et al. Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation. Nat Neurosci, 2018, 21(8): 1096-1106.
26. Zhang K. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory. J Neurosci, 1996, 16(6): 2112-2126.
27. Paul A D, Emma R W, Anna S. A new perspective on the head direction cell system and spatial behavior. Neurosci Biobehav Rev, 2019, 105(2019): 24-33.
28. Chen Guifen, Lu Yi, King J A, et al. Differential influences of environment and self-motion on place and grid cell firing. Nat Commun, 2019, 10(1): 630-640.
29. 李倜. 基于鼠脑海马认知机理的机器人面向目标的导航模型研究. 北京: 北京工业大学, 2016.

Journal of Biomedical Engineering

A grid field calculation model based on perceived speed and perceived angle

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