A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells
文献类型：期刊论文
| 作者 | Gao, Yuanxiang1 |
| 刊名 | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
 |
| 出版日期 | 2023 |
| 卷号 | 17页码:1053097 |
| 关键词 | CONTINUOUS ATTRACTOR NETWORKS TEMPORAL DIFFERENCE PATH-INTEGRATION SYNAPTIC PLASTICITY HIPPOCAMPAL DOPAMINE REWARD REPRESENTATIONS MAPS PREDICTION |
| DOI | 10.3389/fncom.2023.1053097 |
| 英文摘要 | Recent experimental observations have shown that the reactivation of hippocampal place cells (PC) during sleep or wakeful immobility depicts trajectories that can go around barriers and can flexibly adapt to a changing maze layout. However, existing computational models of replay fall short of generating such layout-conforming replay, restricting their usage to simple environments, like linear tracks or open fields. In this paper, we propose a computational model that generates layout-conforming replay and explains how such replay drives the learning of flexible navigation in a maze. First, we propose a Hebbian-like rule to learn the inter-PC synaptic strength during exploration. Then we use a continuous attractor network (CAN) with feedback inhibition to model the interaction among place cells and hippocampal interneurons. The activity bump of place cells drifts along paths in the maze, which models layout-conforming replay. During replay in sleep, the synaptic strengths from place cells to striatal medium spiny neurons (MSN) are learned by a novel dopamine-modulated three-factor rule to store place-reward associations. During goal-directed navigation, the CAN periodically generates replay trajectories from the animal's location for path planning, and the trajectory leading to a maximal MSN activity is followed by the animal. We have implemented our model into a high-fidelity virtual rat in the MuJoCo physics simulator. Extensive experiments have demonstrated that its superior flexibility during navigation in a maze is due to a continuous re-learning of inter-PC and PC-MSN synaptic strength. |
| 学科主题 | Mathematical & Computational Biology ; Neurosciences & Neurology |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/27912]  |
| 专题 | 理论物理研究所_理论物理所1978-2010年知识产出 |
| 作者单位 | 1.Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu, Peoples R China 2.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Yuanxiang. A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells[J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,2023,17:1053097. |
| APA | Gao, Yuanxiang.(2023).A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells.FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,17,1053097. |
| MLA | Gao, Yuanxiang."A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells".FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 17(2023):1053097. |
入库方式： OAI收割
来源：理论物理研究所
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