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Learning to predict future locations with internally generated theta sequences

Representing past, present and future locations is key for spatial navigation. Indeed, within each cycle of the theta oscillation, the population of hippocampal place cells appears to represent trajectories starting behind the current position of the animal and sweeping ahead of it. In particular, w...

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Detalles Bibliográficos
Autores principales: Parra-Barrero, Eloy, Cheng, Sen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10208522/
https://www.ncbi.nlm.nih.gov/pubmed/37172053
http://dx.doi.org/10.1371/journal.pcbi.1011101
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author Parra-Barrero, Eloy
Cheng, Sen
author_facet Parra-Barrero, Eloy
Cheng, Sen
author_sort Parra-Barrero, Eloy
collection PubMed
description Representing past, present and future locations is key for spatial navigation. Indeed, within each cycle of the theta oscillation, the population of hippocampal place cells appears to represent trajectories starting behind the current position of the animal and sweeping ahead of it. In particular, we reported recently that the position represented by CA1 place cells at a given theta phase corresponds to the location where animals were or will be located at a fixed time interval into the past or future assuming the animal ran at its typical, not the current, speed through that part of the environment. This coding scheme leads to longer theta trajectories, larger place fields and shallower phase precession in areas where animals typically run faster. Here we present a mechanistic computational model that accounts for these experimental observations. The model consists of a continuous attractor network with short-term synaptic facilitation and depression that internally generates theta sequences that advance at a fixed pace. Spatial locations are then mapped onto the active units via modified Hebbian plasticity. As a result, neighboring units become associated with spatial locations further apart where animals run faster, reproducing our earlier experimental results. The model also accounts for the higher density of place fields generally observed where animals slow down, such as around rewards. Furthermore, our modeling results reveal that an artifact of the decoding analysis might be partly responsible for the observation that theta trajectories start behind the animal’s current position. Overall, our results shed light on how the hippocampal code might arise from the interplay between behavior, sensory input and predefined network dynamics.
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spelling pubmed-102085222023-05-25 Learning to predict future locations with internally generated theta sequences Parra-Barrero, Eloy Cheng, Sen PLoS Comput Biol Research Article Representing past, present and future locations is key for spatial navigation. Indeed, within each cycle of the theta oscillation, the population of hippocampal place cells appears to represent trajectories starting behind the current position of the animal and sweeping ahead of it. In particular, we reported recently that the position represented by CA1 place cells at a given theta phase corresponds to the location where animals were or will be located at a fixed time interval into the past or future assuming the animal ran at its typical, not the current, speed through that part of the environment. This coding scheme leads to longer theta trajectories, larger place fields and shallower phase precession in areas where animals typically run faster. Here we present a mechanistic computational model that accounts for these experimental observations. The model consists of a continuous attractor network with short-term synaptic facilitation and depression that internally generates theta sequences that advance at a fixed pace. Spatial locations are then mapped onto the active units via modified Hebbian plasticity. As a result, neighboring units become associated with spatial locations further apart where animals run faster, reproducing our earlier experimental results. The model also accounts for the higher density of place fields generally observed where animals slow down, such as around rewards. Furthermore, our modeling results reveal that an artifact of the decoding analysis might be partly responsible for the observation that theta trajectories start behind the animal’s current position. Overall, our results shed light on how the hippocampal code might arise from the interplay between behavior, sensory input and predefined network dynamics. Public Library of Science 2023-05-12 /pmc/articles/PMC10208522/ /pubmed/37172053 http://dx.doi.org/10.1371/journal.pcbi.1011101 Text en © 2023 Parra-Barrero, Cheng https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Parra-Barrero, Eloy
Cheng, Sen
Learning to predict future locations with internally generated theta sequences
title Learning to predict future locations with internally generated theta sequences
title_full Learning to predict future locations with internally generated theta sequences
title_fullStr Learning to predict future locations with internally generated theta sequences
title_full_unstemmed Learning to predict future locations with internally generated theta sequences
title_short Learning to predict future locations with internally generated theta sequences
title_sort learning to predict future locations with internally generated theta sequences
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10208522/
https://www.ncbi.nlm.nih.gov/pubmed/37172053
http://dx.doi.org/10.1371/journal.pcbi.1011101
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