Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model
- Autores
- Fernandez Leon, Jose Alberto; Uysal, Ahmet Kerim; Ji, Daoyun
- Año de publicación
- 2022
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal’s exploration of a square arena. The grid cell model processed the animal’s velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal’s position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal’s current location contributed more to the error reduction than remote place fields. Place cells’ fields encoding space could function as spatial anchoring signals for precise path integration by grid cells.
Fil: Fernandez Leon, Jose Alberto. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina
Fil: Uysal, Ahmet Kerim. Baylor College of Medicine; Estados Unidos
Fil: Ji, Daoyun. Baylor College of Medicine; Estados Unidos - Materia
-
Grid cells
Place cells
Navigation
Path integration - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/213467
Ver los metadatos del registro completo
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Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor modelFernandez Leon, Jose AlbertoUysal, Ahmet KerimJi, DaoyunGrid cellsPlace cellsNavigationPath integrationhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1https://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal’s exploration of a square arena. The grid cell model processed the animal’s velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal’s position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal’s current location contributed more to the error reduction than remote place fields. Place cells’ fields encoding space could function as spatial anchoring signals for precise path integration by grid cells.Fil: Fernandez Leon, Jose Alberto. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Uysal, Ahmet Kerim. Baylor College of Medicine; Estados UnidosFil: Ji, Daoyun. Baylor College of Medicine; Estados UnidosNature2022-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/213467Fernandez Leon, Jose Alberto; Uysal, Ahmet Kerim; Ji, Daoyun; Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model; Nature; Scientific Reports; 12; 1; 12-2022; 1-212045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-022-25863-2info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-022-25863-2info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:03:40Zoai:ri.conicet.gov.ar:11336/213467instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:03:40.662CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model |
title |
Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model |
spellingShingle |
Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model Fernandez Leon, Jose Alberto Grid cells Place cells Navigation Path integration |
title_short |
Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model |
title_full |
Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model |
title_fullStr |
Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model |
title_full_unstemmed |
Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model |
title_sort |
Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model |
dc.creator.none.fl_str_mv |
Fernandez Leon, Jose Alberto Uysal, Ahmet Kerim Ji, Daoyun |
author |
Fernandez Leon, Jose Alberto |
author_facet |
Fernandez Leon, Jose Alberto Uysal, Ahmet Kerim Ji, Daoyun |
author_role |
author |
author2 |
Uysal, Ahmet Kerim Ji, Daoyun |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Grid cells Place cells Navigation Path integration |
topic |
Grid cells Place cells Navigation Path integration |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1 https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
dc.description.none.fl_txt_mv |
Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal’s exploration of a square arena. The grid cell model processed the animal’s velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal’s position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal’s current location contributed more to the error reduction than remote place fields. Place cells’ fields encoding space could function as spatial anchoring signals for precise path integration by grid cells. Fil: Fernandez Leon, Jose Alberto. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina Fil: Uysal, Ahmet Kerim. Baylor College of Medicine; Estados Unidos Fil: Ji, Daoyun. Baylor College of Medicine; Estados Unidos |
description |
Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal’s exploration of a square arena. The grid cell model processed the animal’s velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal’s position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal’s current location contributed more to the error reduction than remote place fields. Place cells’ fields encoding space could function as spatial anchoring signals for precise path integration by grid cells. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/213467 Fernandez Leon, Jose Alberto; Uysal, Ahmet Kerim; Ji, Daoyun; Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model; Nature; Scientific Reports; 12; 1; 12-2022; 1-21 2045-2322 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/213467 |
identifier_str_mv |
Fernandez Leon, Jose Alberto; Uysal, Ahmet Kerim; Ji, Daoyun; Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model; Nature; Scientific Reports; 12; 1; 12-2022; 1-21 2045-2322 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-022-25863-2 info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-022-25863-2 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Nature |
publisher.none.fl_str_mv |
Nature |
dc.source.none.fl_str_mv |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |