Mechanism of action selection and decision-making in the basal ganglia through a connectionist model approach
Identifieur interne : 000640 ( Hal/Curation ); précédent : 000639; suivant : 000641Mechanism of action selection and decision-making in the basal ganglia through a connectionist model approach
Auteurs : Charlotte Héricé [France]Source :
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Abstract
The nervous system structures involved in decision making constitute a circuit formed by the basal ganglia, the cortex, the thalamus and their numerous interconnections. This circuit can be described as a set of loops operating in parallel and interacting at different points. The decisions and therefore the actions of an individual emerge from the interactions between these loops and the plasticity of their connections. These emerging behaviors and arising learning processes are addressed through a closed-loop approach in which the theoretical model is in constant interaction with the environment of the task. To this end, neural modeling and dedicated analysis software tools were developed in the laboratory. We explore here the dynamics of information flows within this circuit through a computational model described at the neuron and synapse level. Taking into account previous experimental observations from primates and earlier computational models, we incrementally developed a network capable of learning to perform behavioral tasks under several protocols and conditions. The result here is a computational model of learning and decision making in the basal ganglia that allows for the testing of experimental hypotheses and also to conduct in silico pathophysiological or pharmacological investigations at the cellular level. The development of this computational model was conducted in parallel with the development of an experimental protocol of decision making and with the adjustment of a model of Parkinson disease in the salamander (Pleurodeles waltlii).
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This circuit can be described as a set of loops operating in parallel and interacting at different points. The decisions and therefore the actions of an individual emerge from the interactions between these loops and the plasticity of their connections. These emerging behaviors and arising learning processes are addressed through a closed-loop approach in which the theoretical model is in constant interaction with the environment of the task. To this end, neural modeling and dedicated analysis software tools were developed in the laboratory. We explore here the dynamics of information flows within this circuit through a computational model described at the neuron and synapse level. Taking into account previous experimental observations from primates and earlier computational models, we incrementally developed a network capable of learning to perform behavioral tasks under several protocols and conditions. The result here is a computational model of learning and decision making in the basal ganglia that allows for the testing of experimental hypotheses and also to conduct in silico pathophysiological or pharmacological investigations at the cellular level. The development of this computational model was conducted in parallel with the development of an experimental protocol of decision making and with the adjustment of a model of Parkinson disease in the salamander (Pleurodeles waltlii).</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Mechanism of action selection and decision-making in the basal ganglia through a connectionist model approach</title><title xml:lang="fr">Mécanismes de la sélection de l'action et de la prise de décision dans les ganglions de la base : approche par un modèle connexionniste</title><author role="aut"><persName><forename type="first">Charlotte</forename><surname>Héricé</surname></persName><email type="md5">f0ba2959999b751fac70d0641a562577</email><email type="domain">gmail.com</email><idno type="halauthorid">1223353</idno><affiliation ref="#struct-188658"></affiliation><affiliation ref="#struct-244085"></affiliation><affiliation ref="#struct-259761"></affiliation></author><editor role="depositor"><persName><forename>Charlotte</forename><surname>Héricé</surname></persName><email type="md5">f0ba2959999b751fac70d0641a562577</email><email type="domain">gmail.com</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2016-11-28 13:10:46</date><date type="whenModified">2016-12-01 01:04:11</date><date type="whenReleased">2016-11-30 15:39:39</date><date type="whenProduced">2016-11-21</date><date type="whenEndEmbargoed">2016-11-28</date><ref type="file" target="https://tel.archives-ouvertes.fr/tel-01402128/document"><date notBefore="2016-11-28"></date></ref><ref type="file" n="1" target="https://tel.archives-ouvertes.fr/tel-01402128/file/Manuscrit_Charlotte_HERICE.pdf"><date notBefore="2016-11-28"></date></ref></edition><respStmt><resp>contributor</resp><name key="306270"><persName><forename>Charlotte</forename><surname>Héricé</surname></persName><email type="md5">f0ba2959999b751fac70d0641a562577</email><email type="domain">gmail.com</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">tel-01402128</idno><idno type="halUri">https://tel.archives-ouvertes.fr/tel-01402128</idno><idno type="halBibtex">herice:tel-01402128</idno><idno type="halRefHtml">Neurosciences [q-bio.NC]. Université de Bordeaux, 2016. Français</idno><idno type="halRef">Neurosciences [q-bio.NC]. Université de Bordeaux, 2016. Français</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="ENSEIRB">Ecole Nationale Supérieure d'Electronique, Informatique et Radiocommunications de Bordeaux</idno><idno type="stamp" n="INRIA-BORDEAUX">INRIA Bordeaux - Sud-Ouest</idno><idno type="stamp" n="UNIV-BORDEAUX">Université de Bordeaux</idno><idno type="stamp" n="LABRI">Laboratoire Bordelais de Recherche en Informatique</idno><idno type="stamp" n="U-BORDEAUX">Université de Bordeaux</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="TESTBORDEAUX">TESTBORDEAUX</idno><idno type="stamp" n="INRIA2">INRIA 2</idno></seriesStmt><notesStmt></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Mechanism of action selection and decision-making in the basal ganglia through a connectionist model approach</title><title xml:lang="fr">Mécanismes de la sélection de l'action et de la prise de décision dans les ganglions de la base : approche par un modèle connexionniste</title><author role="aut"><persName><forename type="first">Charlotte</forename><surname>Héricé</surname></persName><email type="md5">f0ba2959999b751fac70d0641a562577</email><email type="domain">gmail.com</email><idno type="halauthorid">1223353</idno><affiliation ref="#struct-188658"></affiliation><affiliation ref="#struct-244085"></affiliation><affiliation ref="#struct-259761"></affiliation></author></analytic><monogr><imprint><date type="dateDefended">2016-11-21</date></imprint><authority type="institution">Université de Bordeaux</authority><authority type="school">Sciences de la Vie et de la Santé</authority><authority type="supervisor">André Garenne</authority><authority type="jury">Fabrice Wendling [Rapporteur]</authority><authority type="jury">Gwendal Le Masson [Président du jury]</authority><authority type="jury">Pierre Chauvet [Rapporteur]</authority><authority type="jury">André Garenne [Directeur]</authority><authority type="jury">Rémi Dubois [Tuteur]</authority><authority type="jury">Marie Beurton-Aimar [Invité]</authority><authority type="jury">Jean-Marie Cabelguen [Invité]</authority></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Computational neurosciences</term><term xml:lang="en"> connectionism</term><term xml:lang="en"> basal ganglia</term><term xml:lang="en"> decision-making</term><term xml:lang="en"> closed loop</term><term xml:lang="fr">prise de décision</term><term xml:lang="fr">boucle fermée</term><term xml:lang="fr">connexionnisme</term><term xml:lang="fr">ganglions de la base</term><term xml:lang="fr">Neurosciences computationnelles</term></keywords><classCode scheme="halDomain" n="sdv.neu">Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]</classCode><classCode scheme="halDomain" n="info.info-lg">Computer Science [cs]/Machine Learning [cs.LG]</classCode><classCode scheme="halDomain" n="info.info-bi">Computer Science [cs]/Bioinformatics [q-bio.QM]</classCode><classCode scheme="halTypology" n="THESE">Theses</classCode></textClass><abstract xml:lang="en">The nervous system structures involved in decision making constitute a circuit formed by the basal ganglia, the cortex, the thalamus and their numerous interconnections. This circuit can be described as a set of loops operating in parallel and interacting at different points. The decisions and therefore the actions of an individual emerge from the interactions between these loops and the plasticity of their connections. These emerging behaviors and arising learning processes are addressed through a closed-loop approach in which the theoretical model is in constant interaction with the environment of the task. To this end, neural modeling and dedicated analysis software tools were developed in the laboratory. We explore here the dynamics of information flows within this circuit through a computational model described at the neuron and synapse level. Taking into account previous experimental observations from primates and earlier computational models, we incrementally developed a network capable of learning to perform behavioral tasks under several protocols and conditions. The result here is a computational model of learning and decision making in the basal ganglia that allows for the testing of experimental hypotheses and also to conduct in silico pathophysiological or pharmacological investigations at the cellular level. The development of this computational model was conducted in parallel with the development of an experimental protocol of decision making and with the adjustment of a model of Parkinson disease in the salamander (Pleurodeles waltlii).</abstract><abstract xml:lang="fr">Les structures du système nerveux responsables des modalités de la prise de décision forment un circuit constitué par les ganglions de la base, le cortex, le thalamus et leurs nombreuses interconnexions. Ce circuit peut être décrit comme un ensemble de boucles fonctionnant en parallèle et interagissant en différents points. Des interactions entre ces boucles et de la plasticité de leurs connexions émergent les choix et donc les actions d’un individu.Ces comportements émergents et les phénomènes d’apprentissage qui en découlent sont abordés à travers une approche en boucle fermée dans laquelle le modèle théorique est en interaction constante avec l’environnement où se déroule la tâche comportementale étudiée. À cette fin, des outils de modélisation neuronale et d’analyse dédiés ont été développés dans le laboratoire d’accueil.Nous explorons donc ici la dynamique des flux d’information au sein de ce circuit à travers un modèle computationnel décrit à l'échelle du neurone et de la synapse. À partir d'observations expérimentales préalables réalisées sur le primate et de modèles computationnels antérieurs nous avons développé de manière incrémentale un réseau capable d'apprendre à réaliser les tâches comportementales dans plusieurs protocoles et conditions. Le résultat obtenu ici est un modèle computationnel d'apprentissage et de prise de décision dans les ganglions de la base qui permet de tester des hypothèses expérimentales et d’effectuer des investigations physiopathologiques ou pharmacologiques in silico à l'échelle cellulaire. Le développement de ce modèle computationnel a été mené en parallèle avec l'étude expérimentale d'un protocole de prise de décision et la mise au point d'un modèle de maladie de Parkinson chez la salamandre (Pleurodeles waltlii). </abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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