A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells
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 gener...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fncom.2023.1053097/full |
| _version_ | 1811168389466423296 |
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| author | Yuanxiang Gao Yuanxiang Gao |
| author_facet | Yuanxiang Gao Yuanxiang Gao |
| author_sort | Yuanxiang Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T16:25:29Z |
| format | Article |
| id | doaj.art-229d5abd59c4409cb7ca2d523c0086b0 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-10T16:25:29Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-229d5abd59c4409cb7ca2d523c0086b02023-02-09T06:18:54ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882023-02-011710.3389/fncom.2023.10530971053097A computational model of learning flexible navigation in a maze by layout-conforming replay of place cellsYuanxiang Gao0Yuanxiang Gao1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaCAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, ChinaRecent 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.https://www.frontiersin.org/articles/10.3389/fncom.2023.1053097/fullflexible navigationplace cellshippocampal replaymedium spiny neuronsthree-factor learningpath planning |
| spellingShingle | Yuanxiang Gao Yuanxiang Gao A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells Frontiers in Computational Neuroscience flexible navigation place cells hippocampal replay medium spiny neurons three-factor learning path planning |
| title | A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells |
| title_full | A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells |
| title_fullStr | A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells |
| title_full_unstemmed | A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells |
| title_short | A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells |
| title_sort | computational model of learning flexible navigation in a maze by layout conforming replay of place cells |
| topic | flexible navigation place cells hippocampal replay medium spiny neurons three-factor learning path planning |
| url | https://www.frontiersin.org/articles/10.3389/fncom.2023.1053097/full |
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