Model learning based on grid cell representations

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	Model learning based on grid cell representations
	Si BL(斯白露)2 ; Huang GW(黄冠文)1; Tang FZ(唐凤珍)2
	2017
会议日期	December 5-8, 2017
会议地点	Macau, China
页码	1032-1037
英文摘要	Mammals 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.
源文献作者	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
产权排序	2
会议录	Proceedings of the 2017 IEEE International Conference on Robotics and Biomimetics
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-5386-3741-8
内容类型	会议论文
源URL	[http://119.78.100.139/handle/173321/22120]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Huang GW(黄冠文)
作者单位	1.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
推荐引用方式 GB/T 7714	Si BL,Huang GW,Tang FZ. Model learning based on grid cell representations[C]. 见:. Macau, China. December 5-8, 2017.