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Unraveling the neural circuitry of sequence-based navigation using a combined fos imaging and computational approach

Author: Bénédicte BabayanLaure Rondi-ReigFabienne AujardSerge LarocheNadine RavelAll authors
Publisher: 2014.
Dissertation: Thèse de doctorat : Neurosciences : Paris 5 : 2014.
Edition/Format:   Computer file : Document : Thesis/dissertation : English
Summary:
La navigation spatiale est une fonction complexe qui nécessite de combiner des informations sur l'environnement et notre mouvement propre pour construire une représentation du monde et trouver le chemin le plus direct vers notre but. Cette intégration multimodale suggère qu'un large réseau de structures corticales et sous-corticales interagit avec l'hippocampe, structure clé de la navigation. Je me suis
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Genre/Form: Thèses et écrits académiques
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Bénédicte Babayan; Laure Rondi-Reig; Fabienne Aujard; Serge Laroche; Nadine Ravel; Benoît Girard; Hugo Spiers; Alessandro Treves; Université Paris Descartes (1970-2019).; École doctorale Frontières de l'innovation en recherche et éducation (Paris).
OCLC Number: 904011783
Notes: Titre provenant de l'écran-titre.
Description: 1 online resource
Responsibility: Bénédicte Babayan ; sous la direction de Laure Rondi-Reig.

Abstract:

La navigation spatiale est une fonction complexe qui nécessite de combiner des informations sur l'environnement et notre mouvement propre pour construire une représentation du monde et trouver le chemin le plus direct vers notre but. Cette intégration multimodale suggère qu'un large réseau de structures corticales et sous-corticales interagit avec l'hippocampe, structure clé de la navigation. Je me suis concentrée chez la souris sur la navigation de type séquence (ou stratégie égocentrique séquentielle) qui repose sur l'organisation temporelle de mouvements associés à des points de choix spatialement distincts. Après avoir montré que l'apprentissage de cette navigation de type séquence nécessitait l'hippocampe et le striatum dorso-médian, nous avons caractérisé le réseau fonctionnel la sous-tendant en combinant de l'imagerie Fos, de l'analyse de connectivité fonctionnelle et une approche computationnelle. Les réseaux fonctionnels changent au cours de l'apprentissage. Lors de la phase précoce, le réseau impliqué comprend un ensemble de régions cortico-striatales fortement corrélées. L'hippocampe était activé ainsi que des structures impliquées dans le traitement d'informations de mouvement propre (cervelet), dans la manipulation de représentations mentales de l'espace (cortex rétrosplénial, pariétal, entorhinal) et dans la planification de trajectoires dirigées vers un but (boucle cortex préfrontal-ganglions de la base). Le réseau de la phase tardive est caractérisé par l'apparition d'activations coordonnées de l'hippocampe et du cervelet avec le reste du réseau. Parallèlement, nous avons testé si l'intégration de chemin, de l'apprentissage par renforcement basé modèle ou non-basé modèle pouvaient reproduire le comportement des souris. Seul un apprentissage par renforcement non-basé modèle auquel une mémoire rétrospective était ajoutée pouvait reproduire les dynamiques d'apprentissage à l'échelle du groupe ainsi que la variabilité individuelle. Ces résultats suggèrent qu'un modèle d'apprentissage par renforcement suffit à l'apprentissage de la navigation de type séquence et que l'ensemble des structures que cet apprentissage requiert adaptent leurs interactions fonctionnelles au cours de l'apprentissage.

Spatial navigation is a complex function requiring the combination of external and self-motion cues to build a coherent representation of the external world and drive optimal behaviour directed towards a goal. This multimodal integration suggests that a large network of cortical and subcortical structures interacts with the hippocampus, a key structure in navigation. I have studied navigation in mice through this global approach and have focused on one particular type of navigation, which consists in remembering a sequence of turns, named sequence-based navigation or sequential egocentric strategy. This navigation specifically relies on the temporal organization of movements at spatially distinct choice points. We first showed that sequence-based navigation learning required the hippocampus and the dorsomedial striatum. Our aim was to identify the functional network underlying sequence-based navigation using Fos imaging and computational approaches. The functional networks dynamically changed across early and late learning stages. The early stage network was dominated by a highly inter-connected cortico-striatal cluster. The hippocampus was activated alongside structures known to be involved in self-motion processing (cerebellar cortices), in mental representation of space manipulations (retrosplenial, parietal, entorhinal cortices) and in goal-directed path planning (prefrontal-basal ganglia loop). The late stage was characterized by the emergence of correlated activity between the hippocampus, the cerebellum and the cortico-striatal structures. Conjointly, we explored whether path integration, model-based or model-free reinforcement learning algorithms could explain mice's learning dynamics. Only the model-free system, as long as a retrospective memory component was added to it, was able to reproduce both the group learning dynamics and the individual variability observed in the mice. These results suggest that a unique model-free reinforcement learning algorithm was sufficient to learn sequence-based navigation and that the multiple structures this learning required adapted their functional interactions across learning.

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Cette int\u00E9gration multimodale sugg\u00E8re qu\'un large r\u00E9seau de structures corticales et sous-corticales interagit avec l\'hippocampe, structure cl\u00E9 de la navigation. Je me suis concentr\u00E9e chez la souris sur la navigation de type s\u00E9quence (ou strat\u00E9gie \u00E9gocentrique s\u00E9quentielle) qui repose sur l\'organisation temporelle de mouvements associ\u00E9s \u00E0 des points de choix spatialement distincts. Apr\u00E8s avoir montr\u00E9 que l\'apprentissage de cette navigation de type s\u00E9quence n\u00E9cessitait l\'hippocampe et le striatum dorso-m\u00E9dian, nous avons caract\u00E9ris\u00E9 le r\u00E9seau fonctionnel la sous-tendant en combinant de l\'imagerie Fos, de l\'analyse de connectivit\u00E9 fonctionnelle et une approche computationnelle. Les r\u00E9seaux fonctionnels changent au cours de l\'apprentissage. Lors de la phase pr\u00E9coce, le r\u00E9seau impliqu\u00E9 comprend un ensemble de r\u00E9gions cortico-striatales fortement corr\u00E9l\u00E9es. L\'hippocampe \u00E9tait activ\u00E9 ainsi que des structures impliqu\u00E9es dans le traitement d\'informations de mouvement propre (cervelet), dans la manipulation de repr\u00E9sentations mentales de l\'espace (cortex r\u00E9trospl\u00E9nial, pari\u00E9tal, entorhinal) et dans la planification de trajectoires dirig\u00E9es vers un but (boucle cortex pr\u00E9frontal-ganglions de la base). Le r\u00E9seau de la phase tardive est caract\u00E9ris\u00E9 par l\'apparition d\'activations coordonn\u00E9es de l\'hippocampe et du cervelet avec le reste du r\u00E9seau. Parall\u00E8lement, nous avons test\u00E9 si l\'int\u00E9gration de chemin, de l\'apprentissage par renforcement bas\u00E9 mod\u00E8le ou non-bas\u00E9 mod\u00E8le pouvaient reproduire le comportement des souris. Seul un apprentissage par renforcement non-bas\u00E9 mod\u00E8le auquel une m\u00E9moire r\u00E9trospective \u00E9tait ajout\u00E9e pouvait reproduire les dynamiques d\'apprentissage \u00E0 l\'\u00E9chelle du groupe ainsi que la variabilit\u00E9 individuelle. Ces r\u00E9sultats sugg\u00E8rent qu\'un mod\u00E8le d\'apprentissage par renforcement suffit \u00E0 l\'apprentissage de la navigation de type s\u00E9quence et que l\'ensemble des structures que cet apprentissage requiert adaptent leurs interactions fonctionnelles au cours de l\'apprentissage.<\/span>\" ;\u00A0\u00A0\u00A0\nschema:description<\/a> \"Spatial navigation is a complex function requiring the combination of external and self-motion cues to build a coherent representation of the external world and drive optimal behaviour directed towards a goal. This multimodal integration suggests that a large network of cortical and subcortical structures interacts with the hippocampus, a key structure in navigation. I have studied navigation in mice through this global approach and have focused on one particular type of navigation, which consists in remembering a sequence of turns, named sequence-based navigation or sequential egocentric strategy. This navigation specifically relies on the temporal organization of movements at spatially distinct choice points. We first showed that sequence-based navigation learning required the hippocampus and the dorsomedial striatum. Our aim was to identify the functional network underlying sequence-based navigation using Fos imaging and computational approaches. The functional networks dynamically changed across early and late learning stages. The early stage network was dominated by a highly inter-connected cortico-striatal cluster. The hippocampus was activated alongside structures known to be involved in self-motion processing (cerebellar cortices), in mental representation of space manipulations (retrosplenial, parietal, entorhinal cortices) and in goal-directed path planning (prefrontal-basal ganglia loop). The late stage was characterized by the emergence of correlated activity between the hippocampus, the cerebellum and the cortico-striatal structures. Conjointly, we explored whether path integration, model-based or model-free reinforcement learning algorithms could explain mice\'s learning dynamics. Only the model-free system, as long as a retrospective memory component was added to it, was able to reproduce both the group learning dynamics and the individual variability observed in the mice. 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<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Person\/aujard_fabienne_19<\/a>> # Fabienne Aujard<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:birthDate<\/a> \"19..<\/span>\" ;\u00A0\u00A0\u00A0\nschema:deathDate<\/a> \"\" ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Aujard<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Fabienne<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Fabienne Aujard<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Person\/babayan_benedicte_1988<\/a>> # B\u00E9n\u00E9dicte Babayan<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:birthDate<\/a> \"1988<\/span>\" ;\u00A0\u00A0\u00A0\nschema:deathDate<\/a> \"\" ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Babayan<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"B\u00E9n\u00E9dicte<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"B\u00E9n\u00E9dicte Babayan<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Person\/girard_benoit<\/a>> # Beno\u00EEt Girard<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Girard<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Beno\u00EEt<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Beno\u00EEt Girard<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Person\/laroche_serge<\/a>> # Serge Laroche<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Laroche<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Serge<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Serge Laroche<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Person\/ravel_nadine_1960<\/a>> # Nadine Ravel<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:birthDate<\/a> \"1960<\/span>\" ;\u00A0\u00A0\u00A0\nschema:deathDate<\/a> \"\" ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Ravel<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Nadine<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Nadine Ravel<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Person\/rondi_reig_laure_1969<\/a>> # Laure Rondi-Reig<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:birthDate<\/a> \"1969<\/span>\" ;\u00A0\u00A0\u00A0\nschema:deathDate<\/a> \"\" ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Rondi-Reig<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Laure<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Laure Rondi-Reig<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Person\/spiers_hugo<\/a>> # Hugo Spiers<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Spiers<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Hugo<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Hugo Spiers<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Person\/treves_alessandro<\/a>> # Alessandro Treves<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Treves<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Alessandro<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Alessandro Treves<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Thing\/analyse_de_reseau<\/a>> # Analyse de r\u00E9seau<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Analyse de r\u00E9seau<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Thing\/apprentissage_computationnel<\/a>> # Apprentissage computationnel<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Apprentissage computationnel<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Thing\/apprentissage_de_sequence<\/a>> # Apprentissage de s\u00E9quence<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Apprentissage de s\u00E9quence<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Thing\/apprentissage_par_renforcement<\/a>> # Apprentissage par renforcement<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Apprentissage par renforcement<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Thing\/comportement<\/a>> # Comportement<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Comportement<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Thing\/imagerie_fos<\/a>> # Imagerie Fos<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Imagerie Fos<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Thing\/navigation<\/a>> # Navigation<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Navigation<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Topic\/cervelet<\/a>> # Cervelet<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Intangible<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Cervelet<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Topic\/hippocampe_anatomie<\/a>> # Hippocampe (anatomie)<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Intangible<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Hippocampe (anatomie)<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Topic\/intelligence_computationnelle<\/a>> # Intelligence computationnelle<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Intangible<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Intelligence computationnelle<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/2302026147#Topic\/perception_spatiale<\/a>> # Perception spatiale<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Intangible<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Perception spatiale<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/id.loc.gov\/vocabulary\/countries\/fr<\/a>>\u00A0\u00A0\u00A0\u00A0a \nschema:Place<\/a> ;\u00A0\u00A0\u00A0\ndcterms:identifier<\/a> \"fr<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/www.theses.fr\/2014PA05T059\/document<\/a>>\u00A0\u00A0\u00A0\nrdfs:comment<\/a> \"Acc\u00E8s au texte int\u00E9gral<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/www.worldcat.org\/title\/-\/oclc\/904011783<\/a>>\u00A0\u00A0\u00A0\u00A0a \ngenont:InformationResource<\/a>, genont:ContentTypeGenericResource<\/a> ;\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/www.worldcat.org\/oclc\/904011783<\/a>> ; # Unraveling the neural circuitry of sequence-based navigation using a combined fos imaging and computational approach<\/span>\n\u00A0\u00A0\u00A0\nschema:dateModified<\/a> \"2020-04-17<\/span>\" ;\u00A0\u00A0\u00A0\nvoid:inDataset<\/a> <http:\/\/purl.oclc.org\/dataset\/WorldCat<\/a>> ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n\n

Content-negotiable representations<\/p>\n