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Model learning based on grid cell representations
Huang GW(黄冠文)1; Si BL(斯白露)2; Tang FZ(唐凤珍)2
Department机器人学研究室
Conference Name2017 IEEE International Conference on Robotics and Biomimetics, ROBIO 2017
Conference DateDecember 5-8, 2017
Conference PlaceMacau, China
Author of SourceBeijing Institute of Technology ; City University of Hong Kong ; IEEE Robotics and Automation Society ; Shenzhen Academy of Robotics ; University of Hong Kong ; University of Macau
Source PublicationProceedings of the 2017 IEEE International Conference on Robotics and Biomimetics
PublisherIEEE
Publication PlaceNew York
2017
Pages1032-1037
Indexed ByEI
EI Accession number20182905561192
Contribution Rank2
ISBN978-1-5386-3741-8
AbstractMammals are able to form internal representations of their environments. Place cells found in the hippocampus fire stingily only at a couple of locations of the environment. One synapse away from the hippocampus, grid cells in medial entorhinal cortex discharge bountifully at many locations of the environment, expressing periodic triangular grid firing maps in two-dimensional open field maze. In this study, we investigate the functional advantage of grid codes in the hippocampal-entorhinal circuit from the perspective of model learning. We build neural network models to learn the mapping from space to an abstract variable, which could be used in cognitive processes such as decision-making or motor control. The network using grid code as spatial input achieves better learning accuracy with fewer number of cells than the radial basis function network, which assumes place cell inputs. Our result shows that grid representations constitute better spatial representation in the task of model learning, and may help associative cortex better read out the information held in memory circuits.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22120
Collection机器人学研究室
Corresponding AuthorHuang GW(黄冠文)
Affiliation1.Automation and Electrical Engineering Department, Shenyang Ligong University, Shenyang, China
2.State Key Laboratory of Robotics, Shenyang Institute Of Automation, Chinese Academy Of Science, Shenyang, China
Recommended Citation
GB/T 7714
Huang GW,Si BL,Tang FZ. Model learning based on grid cell representations[C]//Beijing Institute of Technology, City University of Hong Kong, IEEE Robotics and Automation Society, Shenzhen Academy of Robotics, University of Hong Kong, University of Macau. New York:IEEE,2017:1032-1037.
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