Spatial navigation method based on the entorhinal-hippocampal-prefrontal information transmission circuit of rat’s brain_Journal of Biomedical Engineering

Authors：

LIAO Yishen ,  YU Naigong

1. 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; Action neurons; Entorhinal-hippocampal; Prefrontal cortex; Spatial navigation

DOI：

10.7507/1001-5515.202303047

Video：

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Abstract Full text Figures/Tables Video References Cited by

Physiological studies have revealed that rats perform spatial localization relying on grid cells and place cells in the entorhinal-hippocampal CA3 structure. The dynamic connection between the entorhinal-hippocampal structure and the prefrontal cortex is crucial for navigation. Based on these findings, this paper proposes a spatial navigation method based on the entorhinal-hippocampal-prefrontal information transmission circuit of the rat’s brain, with the aim of endowing the mobile robot with strong spatial navigation capability. Using the hippocampal CA3-prefrontal spatial navigation model as a foundation, this paper constructed a dynamic self-organizing model with the hippocampal CA1 place cells as the basic unit to optimize the navigation path. The path information was then fed back to the impulse neural network via hippocampal CA3 place cells and prefrontal cortex action neurons, improving the convergence speed of the model and helping to establish long-term memory of navigation habits. To verify the validity of the method, two-dimensional simulation experiments and three-dimensional simulation robot experiments were designed in this paper. The experimental results showed that the method presented in this paper not only surpassed other algorithms in terms of navigation efficiency and convergence speed, but also exhibited good adaptability to dynamic navigation tasks. Furthermore, our method can be effectively applied to mobile robots.

Citation： LIAO Yishen, YU Naigong. Spatial navigation method based on the entorhinal-hippocampal-prefrontal information transmission circuit of rat’s brain. Journal of Biomedical Engineering, 2024, 41(1): 80-89. doi: 10.7507/1001-5515.202303047 Copy

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2.	Wu Q, Gong X, Xu K, et al. Towards target-driven visual navigation in indoor scenes via generative imitation learning. IEEE Robot Autom Lett, 2020, 6(1): 175-182.
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13.	Yu N, Zhai Y, Yuan Y, et al. A bionic robot navigation algorithm based on cognitive mechanism of hippocampus. IEEE Trans Autom Sci Eng, 2019, 16(4): 1640-1652.
14.	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.
15.	Liu D, Lyu Z, Zou Q, et al. Robotic navigation based on experiences and predictive map inspired by spatial cognition. IEEE ASME Trans Mechatron, 2022, 27(6): 4316-4326.
16.	Oudeyer P Y, Kaplan F. Intelligent adaptive curiosity: A source of self-development// Procedings of the International Workshop on Epigenetic Robotics. Lund: Lund University Cognitive Studies, 2004: 127-130.
17.	张晓平, 阮晓钢, 肖尧, 等. 基于内发动机机制的移动机器人自主路径规划方法. 控制与决策, 2018, 33(9): 1605-1611.
18.	阮晓钢, 张家辉, 黄静, 等. 一种结合内在动机理论的移动机器人环境认知模型. 控制与决策, 2021, 36(9): 2211-2217.
19.	Kulvicius T, Tamosiunaite M, Ainge J, et al. Odor supported place cell model and goal navigation in rodents. J Comput Neurosci, 2008, 25(3): 481-500.
20.	Frémaux N, Sprekeler H, Gerstner W. Reinforcement learning using a continuous time actor-critic framework with spiking neurons. PLoS Comput Biol, 2013, 9(4): e1003024.
21.	Brzosko Z, Zannone S, Schultz W, et al. Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation. Elife, 2017, 6: e27756.
22.	Ang G W Y, Tang C S, Hay Y A, et al. The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning. PLoS Comput Biol, 2021, 17(6): e1009017.
23.	于乃功, 廖诣深. 基于鼠脑内嗅—海马认知机制的移动机器人空间定位模型. 生物医学工程学杂志, 2022, 39(2): 217-227.
24.	Bjerknes T L, Moser E I, Moser M B. Representation of geometric borders in the developing rat. Neuron, 2014, 82(1): 71-78.
25.	Adam S, Busoniu L, Babuska R. Experience replay for real-time reinforcement learning control. IEEE Trans Syst Man Cybern, 2011, 42(2): 201-212.

1. Banino A, Barry C, Uria B, et al. Vector-based navigation using grid-like representations in artificial agents. Nature, 2018, 557(7705): 429-433.
2. Wu Q, Gong X, Xu K, et al. Towards target-driven visual navigation in indoor scenes via generative imitation learning. IEEE Robot Autom Lett, 2020, 6(1): 175-182.
3. Ormond J, O’Keefe J. Hippocampal place cells have goal-oriented vector fields during navigation. Nature, 2022, 607(7920): 741-746.
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. Hafting T, Fyhn M, Molden S, et al. Microstructure of a spatial map in the entorhinal cortex. Nature, 2005, 436(7052): 801-806.
6. Solstad T, Boccara C N, Kropff E, et al. Representation of geometric borders in the entorhinal cortex. Science, 2008, 322(5909): 1865-1868.
7. Taube J S, Muller R U, Ranck J B. Head-direction cells recorded from the postsubiculum in freely moving rats. II. Effects of environmental manipulations. J Neurosci, 1990, 10(2): 436-447.
8. Aziz A, Sreeharsha P S S, Natesh R, et al. An integrated deep learning‐based model of spatial cells that combines self‐motion with sensory information. Hippocampus, 2022, 32(10): 716-730.
9. Monteiro J, Pedro A, Silva A J. A Gray Code model for the encoding of grid cells in the Entorhinal Cortex. Neural Comput Appl, 2022, 34(3): 2287-2306.
10. Li T, Arleo A, Sheynikhovich D. Modeling place cells and grid cells in multi-compartment environments: Entorhinal–hippocampal loop as a multisensory integration circuit. Neur Netw, 2020, 121: 37-51.
11. Patai E Z, Javadi A H, Ozubko J D, et al. Hippocampal and retrosplenial goal distance coding after long-term consolidation of a real-world environment. Cereb Cortex, 2019, 29(6): 2748-2758.
12. Javadi A H, Emo B, Howard L R, et al. Hippocampal and prefrontal processing of network topology to simulate the future. Nat Commun, 2017, 8(1): 1-11.
13. Yu N, Zhai Y, Yuan Y, et al. A bionic robot navigation algorithm based on cognitive mechanism of hippocampus. IEEE Trans Autom Sci Eng, 2019, 16(4): 1640-1652.
14. 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.
15. Liu D, Lyu Z, Zou Q, et al. Robotic navigation based on experiences and predictive map inspired by spatial cognition. IEEE ASME Trans Mechatron, 2022, 27(6): 4316-4326.
16. Oudeyer P Y, Kaplan F. Intelligent adaptive curiosity: A source of self-development// Procedings of the International Workshop on Epigenetic Robotics. Lund: Lund University Cognitive Studies, 2004: 127-130.
17. 张晓平, 阮晓钢, 肖尧, 等. 基于内发动机机制的移动机器人自主路径规划方法. 控制与决策, 2018, 33(9): 1605-1611.
18. 阮晓钢, 张家辉, 黄静, 等. 一种结合内在动机理论的移动机器人环境认知模型. 控制与决策, 2021, 36(9): 2211-2217.
19. Kulvicius T, Tamosiunaite M, Ainge J, et al. Odor supported place cell model and goal navigation in rodents. J Comput Neurosci, 2008, 25(3): 481-500.
20. Frémaux N, Sprekeler H, Gerstner W. Reinforcement learning using a continuous time actor-critic framework with spiking neurons. PLoS Comput Biol, 2013, 9(4): e1003024.
21. Brzosko Z, Zannone S, Schultz W, et al. Sequential neuromodulation of Hebbian plasticity offers mechanism for effective reward-based navigation. Elife, 2017, 6: e27756.
22. Ang G W Y, Tang C S, Hay Y A, et al. The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning. PLoS Comput Biol, 2021, 17(6): e1009017.
23. 于乃功, 廖诣深. 基于鼠脑内嗅—海马认知机制的移动机器人空间定位模型. 生物医学工程学杂志, 2022, 39(2): 217-227.
24. Bjerknes T L, Moser E I, Moser M B. Representation of geometric borders in the developing rat. Neuron, 2014, 82(1): 71-78.
25. Adam S, Busoniu L, Babuska R. Experience replay for real-time reinforcement learning control. IEEE Trans Syst Man Cybern, 2011, 42(2): 201-212.

Journal of Biomedical Engineering

Spatial navigation method based on the entorhinal-hippocampal-prefrontal information transmission circuit of rat’s brain

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