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A unified dynamic neural field model of goal directed eye-movements

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  • Additional Information
    • Contributors:
      Statistique pour le Vivant et l’Homme (SVH); Laboratoire Jean Kuntzmann (LJK); Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 )-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 ); Institut de Neurosciences de la Timone (INT); Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS); ANR-10-LABX-0016,IMoBS3,Innovative Mobility : Smart and Sustainable Solutions(2010); ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011)
    • Publication Information:
      CCSD
      Taylor & Francis
    • Publication Date:
      2018
    • Collection:
      Université Grenoble Alpes: HAL
    • Abstract:
      International audience ; Primates heavily rely on their visual system, which exploits signals of graded precision based on the eccentricity of the target in the visual field. The interactions with the environment involve actively selecting and focusing on visual targets or regions of interest, instead of contemplating an omnidirectional visual flow. Eye-movements specifically allow foveating targets and track their motion. Once a target is brought within the central visual field, eye-movements are usually classified into catch-up saccades (jumping from one orientation or fixation to another) and smooth pursuit (continuously tracking a target with low velocity). Building on existing dynamic neural field equations, we introduce a novel model that incorporates internal projections to better estimate the current target location (associated to a peak of activity). Such estimate is then used to trigger an eye movement, leading to qualitatively different behaviors depending on the dynamics of the whole oculomotor system: 1) fixational eye-movements due to small variations in the weights of projections when the target is stationary , 2) interceptive and catch-up saccades when peaks build and relax on the neural field, 3) smooth pursuit when the peak stabilizes near the center of the field, the system reaching a fixed point attractor. Learning is nevertheless required for tracking a rapidly moving target , and the proposed model thus replicates recent results in the monkey, in which repeated exercise permits the maintenance of the target within in the central visual field at its current (here-and-now) location, despite the delays involved in transmitting retinal signals to the oculomotor neurons.
    • Accession Number:
      10.1080/09540091.2017.1351421
    • Online Access:
      https://hal.science/hal-01637024
      https://hal.science/hal-01637024v1/document
      https://hal.science/hal-01637024v1/file/2017%20-%20Connection%20Science%20-%20Quinton,%20Goffart%20-%20A%20unified%20dynamic%20neural%20field%20model%20of%20goal%20directed%20eye-movements.pdf
      https://doi.org/10.1080/09540091.2017.1351421
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.C702432C