SIA OpenIR  > 机器人学研究室
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
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
Publication PlaceNew York
Indexed ByEI
EI Accession number20182905561192
Contribution Rank2
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.
Document Type会议论文
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.
Files in This Item:
File Name/Size DocType Version Access License
Model learning based(3560KB)会议论文 开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Huang GW(黄冠文)]'s Articles
[Si BL(斯白露)]'s Articles
[Tang FZ(唐凤珍)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Huang GW(黄冠文)]'s Articles
[Si BL(斯白露)]'s Articles
[Tang FZ(唐凤珍)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Huang GW(黄冠文)]'s Articles
[Si BL(斯白露)]'s Articles
[Tang FZ(唐凤珍)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Model learning based on grid cell representations.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.