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Zhang Z, Tang F, Li Y, Feng X. A spatial transformation-based CAN model for information integration within grid cell modules. Cogn Neurodyn 2024; 18:1861-1876. [PMID: 39104694 PMCID: PMC11297887 DOI: 10.1007/s11571-023-10047-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 10/13/2023] [Accepted: 11/26/2023] [Indexed: 08/07/2024] Open
Abstract
The hippocampal-entorhinal circuit is considered to play an important role in the spatial cognition of animals. However, the mechanism of the information flow within the circuit and its contribution to the function of the grid-cell module are still topics of discussion. Prevailing theories suggest that grid cells are primarily influenced by self-motion inputs from the Medial Entorhinal Cortex, with place cells serving a secondary role by contributing to the visual calibration of grid cells. However, recent evidence suggests that both self-motion inputs and visual cues may collaboratively contribute to the formation of grid-like patterns. In this paper, we introduce a novel Continuous Attractor Network model based on a spatial transformation mechanism. This mechanism enables the integration of self-motion inputs and visual cues within grid-cell modules, synergistically driving the formation of grid-like patterns. From the perspective of individual neurons within the network, our model successfully replicates grid firing patterns. From the view of neural population activity within the network, the network can form and drive the activated bump, which describes the characteristic feature of grid-cell modules, namely, path integration. Through further exploration and experimentation, our model can exhibit significant performance in path integration. This study provides a new insight into understanding the mechanism of how the self-motion and visual inputs contribute to the neural activity within grid-cell modules. Furthermore, it provides theoretical support for achieving accurate path integration, which holds substantial implications for various applications requiring spatial navigation and mapping.
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Affiliation(s)
- Zhihui Zhang
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, 230026 Anhui China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
| | - Fengzhen Tang
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
| | - Yiping Li
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
| | - Xisheng Feng
- The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, No.114, Nanta Street Heping District, Shenyang, 110016 Liaoning China
- University of Science and Technology of China, No.96, JinZhai Road Baohe District, Hefei, 230026 Anhui China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, No.135, Chuangxin Road Hunnan District, Shenyang, 110169 Liaoning China
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Aziz A, Patil BK, Lakshmikanth K, Sreeharsha PSS, Mukhopadhyay A, Chakravarthy VS. Modeling hippocampal spatial cells in rodents navigating in 3D environments. Sci Rep 2024; 14:16714. [PMID: 39030197 PMCID: PMC11271631 DOI: 10.1038/s41598-024-66755-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 07/03/2024] [Indexed: 07/21/2024] Open
Abstract
Studies on the neural correlates of navigation in 3D environments are plagued by several issues that need to be solved. For example, experimental studies show markedly different place cell responses in rats and bats, both navigating in 3D environments. In this study, we focus on modelling the spatial cells in rodents in a 3D environment. We propose a deep autoencoder network to model the place and grid cells in a simulated agent navigating in a 3D environment. The input layer to the autoencoder network model is the HD layer, which encodes the agent's HD in terms of azimuth (θ) and pitch angles (ϕ). The output of this layer is given as input to the Path Integration (PI) layer, which computes displacement in all the preferred directions. The bottleneck layer of the autoencoder model encodes the spatial cell-like responses. Both grid cell and place cell-like responses are observed. The proposed model is verified using two experimental studies with two 3D environments. This model paves the way for a holistic approach using deep neural networks to model spatial cells in 3D navigation.
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Affiliation(s)
- Azra Aziz
- Computational Neuroscience Lab, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Bharat K Patil
- Computational Neuroscience Lab, Indian Institute of Technology Madras, Chennai, 600036, India
| | - Kailash Lakshmikanth
- Computational Neuroscience Lab, Indian Institute of Technology Madras, Chennai, 600036, India
| | | | - Ayan Mukhopadhyay
- Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India
- Instituto de Física, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - V Srinivasa Chakravarthy
- Computational Neuroscience Lab, Indian Institute of Technology Madras, Chennai, 600036, India.
- Center for Complex Systems and Dynamics, Indian Institute of Technology Madras, Chennai, 600036, India.
- Department of Biotechnology, Indian Institute of Technology Madras, Chennai, 600036, India.
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Yu N, Liao Y, Yu H, Sie O. Construction of the rat brain spatial cell firing model on a quadruped robot. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2022. [DOI: 10.1049/cit2.12091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Naigong Yu
- Faculty of Information Technology Beijing University of Technology Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
- Engineering Research Center of Digital Community Ministry of Education Beijing China
| | - Yishen Liao
- Faculty of Information Technology Beijing University of Technology Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
- Engineering Research Center of Digital Community Ministry of Education Beijing China
| | - Hejie Yu
- Faculty of Information Technology Beijing University of Technology Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
- Engineering Research Center of Digital Community Ministry of Education Beijing China
| | - Ouattara Sie
- Faculty of Information Technology Beijing University of Technology Beijing China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China
- Engineering Research Center of Digital Community Ministry of Education Beijing China
- College of Robotic Université Félix Houphouët‐Boigny Abidjan Côte d'Ivoire
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