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Modeling Hippocampal Replay in a Continuous Attractor Neural Network.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling Hippocampal Replay in a Continuous Attractor Neural Network./
作者:	Geng, Ningyao.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	68 p.
附註:	Source: Masters Abstracts International, Volume: 83-12.
Contained By:	Masters Abstracts International83-12.
標題:	Neurosciences. -
ISBN:	9798834020547

Modeling Hippocampal Replay in a Continuous Attractor Neural Network.
Geng, Ningyao.

Modeling Hippocampal Replay in a Continuous Attractor Neural Network. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 68 p.

Source: Masters Abstracts International, Volume: 83-12.

Thesis (M.S.)--Indiana University, 2022.

This item must not be sold to any third party vendors.

Awake hippocampal replay is theorized to underlie planning and decision-making. However, the link between replay contents and animal behavior is weak. Furthermore, replay has been exclusively examined in spatial contexts despite the fact that most replay contents cannot be decoded to represent continuous spatial trajectories. An outstanding question is if a spatial view of the hippocampus can be fully compatible with the rich properties of memory replay. Here, we examined how a spatial paradigm of the hippocampus can be modified to account for memory replay. To this end, we modified a continuous attractor neural network (CANN) for path integration and tested its ability to generate replay-like trajectories. We hypothesized that 1) reducing the inhibition to CA3 place cells and 2) reducing the stabilizing inputs from the place cell system, as well as increasing the inputs from the integration system could prompt the system to generate replay. We found that while reducing inhibition alone was insufficient for replay, modifying the relative inputs from the place cell system and the integration system was successful in generating replay. Our result suggests that the generation of replay in the CANN model is dependent on an internal sense of movement and involves making predictions about upcoming trajectories. The kind of replay generated by the model is compatible with replay serving the role of planning. Overall, our results suggest that a low-dimensional spatial model can be modified to account for properties of memory replay.

ISBN: 9798834020547Subjects--Topical Terms:

588700
Neurosciences.
Subjects--Index Terms:

Computational modeling

Modeling Hippocampal Replay in a Continuous Attractor Neural Network.
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520 $a Awake hippocampal replay is theorized to underlie planning and decision-making. However, the link between replay contents and animal behavior is weak. Furthermore, replay has been exclusively examined in spatial contexts despite the fact that most replay contents cannot be decoded to represent continuous spatial trajectories. An outstanding question is if a spatial view of the hippocampus can be fully compatible with the rich properties of memory replay. Here, we examined how a spatial paradigm of the hippocampus can be modified to account for memory replay. To this end, we modified a continuous attractor neural network (CANN) for path integration and tested its ability to generate replay-like trajectories. We hypothesized that 1) reducing the inhibition to CA3 place cells and 2) reducing the stabilizing inputs from the place cell system, as well as increasing the inputs from the integration system could prompt the system to generate replay. We found that while reducing inhibition alone was insufficient for replay, modifying the relative inputs from the place cell system and the integration system was successful in generating replay. Our result suggests that the generation of replay in the CANN model is dependent on an internal sense of movement and involves making predictions about upcoming trajectories. The kind of replay generated by the model is compatible with replay serving the role of planning. Overall, our results suggest that a low-dimensional spatial model can be modified to account for properties of memory replay.
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