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Learning Cognitive Map Representations for Navigation by Sensory–Motor Integration


Abstract:

How to transform a mixed flow of sensory and motor information into memory state of self-location and to build map representations of the environment are central question...Show More

Abstract:

How to transform a mixed flow of sensory and motor information into memory state of self-location and to build map representations of the environment are central questions in the navigation research. Studies in neuroscience have shown that place cells in the hippocampus of the rodent brains form dynamic cognitive representations of locations in the environment. We propose a neural-network model called sensory–motor integration network model (SeMINet) to learn cognitive map representations by integrating sensory and motor information while an agent is exploring a virtual environment. This biologically inspired model consists of a deep neural network representing visual features of the environment, a recurrent network of place units encoding spatial information by sensorimotor integration, and a secondary network to decode the locations of the agent from spatial representations. The recurrent connections between the place units sustain an activity bump in the network without the need of sensory inputs, and the asymmetry in the connections propagates the activity bump in the network, forming a dynamic memory state which matches the motion of the agent. A competitive learning process establishes the association between the sensory representations and the memory state of the place units, and is able to correct the cumulative path-integration errors. The simulation results demonstrate that the network forms neural codes that convey location information of the agent independent of its head direction. The decoding network reliably predicts the location even when the movement is subject to noise. The proposed SeMINet thus provides a brain-inspired neural-network model for cognitive map updated by both self-motion cues and visual cues.
Published in: IEEE Transactions on Cybernetics ( Volume: 52, Issue: 1, January 2022)
Page(s): 508 - 521
Date of Publication: 03 April 2020

ISSN Information:

PubMed ID: 32275629

Funding Agency:

Citations are not available for this document.

Cites in Papers - |

Cites in Papers - IEEE (5)

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1.
Jia-Hsun Lo, Han-Pang Huang, Yen-Ching Chen, Jen-Hau Chen, "Memory Robot Design: A New Perspective From Human Brain Model and Large Language Model", IEEE Access, vol.13, pp.28539-28549, 2025.
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Yudi Chen, Zhi Xiong, Jianye Liu, "Brain-Inspired Multisensor Navigation Information Fusion Model Based on Spatial Representation Cells", IEEE Sensors Journal, vol.24, no.11, pp.18122-18132, 2024.
3.
Zihui Tang, Xiaoping Wang, Chao Yang, Zhanfei Chen, Zhigang Zeng, "A Bionic Localization Memristive Circuit Based on Spatial Cognitive Mechanisms of Hippocampus and Entorhinal Cortex", IEEE Transactions on Biomedical Circuits and Systems, vol.18, no.3, pp.552-563, 2024.
4.
Hang Liu, Menghan Hu, Yuzhen Chen, Qingli Li, Guangtao Zhai, Simon X. Yang, Xiao-Ping Zhang, Xiaokang Yang, "Angel’s Girl for Blind Painters: An Efficient Painting Navigation System Validated by Multimodal Evaluation Approach", IEEE Transactions on Multimedia, vol.25, pp.2415-2429, 2023.
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Li Weilong, Wu Dewei, Zhu Haonan, Wu Huaxing, Zhao Boxin, Dai Chuanjin, "A Bionic Simultaneous Location and Mapping with Closed-Loop Correction Based on Dynamic Recognition Threshold", 2021 33rd Chinese Control and Decision Conference (CCDC), pp.737-742, 2021.

Cites in Papers - Other Publishers (5)

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Yan Yan Zhang, Jin Yu Hu, Qian Ling, San Hua Xu, Min Kang, Hong Wei, Jie Zou, Quanyong Yi, Gang Tan, Yi Shao, "The amplitude of low frequency fluctuation and spontaneous brain activity alterations in age-related macular degeneration", Frontiers in Medicine, vol.11, 2025.
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Dongye Zhao, Bailu Si, "Formation of cognitive maps in large-scale environments by sensorimotor integration", Cognitive Neurodynamics, vol.19, no.1, 2025.
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Yishen Liao, Naigong Yu, Jinhan Yan, "A Navigation Path Search and Optimization Method for Mobile Robots Based on the Rat Brain’s Cognitive Mechanism", Biomimetics, vol.8, no.5, pp.427, 2023.
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Chaojuan Huang, Xia Zhou, Mengmeng Ren, Wei Zhang, Ke Wan, Jiabin Yin, Mingxu Li, Zhiwei Li, Xiaoqun Zhu, Zhongwu Sun, "Altered dynamic functional network connectivity and topological organization variance in patients with white matter hyperintensities", Journal of Neuroscience Research, 2023.

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