Learning Cognitive Map Representations for Navigation by Sensory-Motor Integration | |
Zhao DY(赵冬晔)3,4,5; Zhang, Zheng6; Lu H(路红)7; Cheng, Sen2; Si BL(斯白露)1; Feng XS(封锡盛)3,4 | |
刊名 | IEEE TRANSACTIONS ON CYBERNETICS |
2022 | |
卷号 | 52期号:1页码:508-521 |
关键词 | Visualization Navigation Robot sensing systems Brain modeling Hippocampus Biological system modeling Cognitive map hippocampus navigation path integration place cells self-motion cues sensorimotor integration sensory-motor integration network model (SeMINet) visual cues |
ISSN号 | 2168-2267 |
产权排序 | 1 |
英文摘要 | 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. |
资助项目 | National Key Research and Development Program of China[2016YFC0801808] ; German Research Foundation[SFB 1280] |
WOS关键词 | HIPPOCAMPAL PLACE CELLS ; PATH-INTEGRATION ; GRID CELLS ; CORTEX ; PERCEPTION ; NETWORKS ; DYNAMICS ; NEURONS ; SYSTEM ; FIELDS |
WOS研究方向 | Automation & Control Systems ; Computer Science |
语种 | 英语 |
WOS记录号 | WOS:000742182700046 |
资助机构 | National Key Research and Development Program of China [2016YFC0801808] ; German Research FoundationGerman Research Foundation (DFG) [SFB 1280] |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/30329] |
专题 | 沈阳自动化研究所_机器人学研究室 |
通讯作者 | Si BL(斯白露) |
作者单位 | 1.School of Systems Science, Beijing Normal University, Beijing 100875, China 2.Institute for Neuroinformatics, Ruhr-Universität Bochum, 44801 Bochum, Germany 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 5.University of Chinese Academy of Sciences, Beijing 100049, China 6.Department of Computer Science, New York University Shanghai, Shanghai 316021, China 7.School of Computer Science, Fudan University, Shanghai 200433, China |
推荐引用方式 GB/T 7714 | Zhao DY,Zhang, Zheng,Lu H,et al. Learning Cognitive Map Representations for Navigation by Sensory-Motor Integration[J]. IEEE TRANSACTIONS ON CYBERNETICS,2022,52(1):508-521. |
APA | Zhao DY,Zhang, Zheng,Lu H,Cheng, Sen,Si BL,&Feng XS.(2022).Learning Cognitive Map Representations for Navigation by Sensory-Motor Integration.IEEE TRANSACTIONS ON CYBERNETICS,52(1),508-521. |
MLA | Zhao DY,et al."Learning Cognitive Map Representations for Navigation by Sensory-Motor Integration".IEEE TRANSACTIONS ON CYBERNETICS 52.1(2022):508-521. |
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