Model learning based on grid cell representations
文献类型：会议论文
| 作者 | Si BL(斯白露)2 ; Huang GW(黄冠文)1; Tang FZ(唐凤珍)2
|
| 出版日期 | 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 |
| 源URL | [http://ir.sia.cn/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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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