• DocumentCode
    1749209
  • Title

    Sequence learning and timing in hippocampus, prefrontal cortex, and accumbens

  • Author

    Banquet, Jean Paul ; Gaussier, Philippe ; Revel, Arnaud ; Moga, S. ; Burnod, Yves

  • Author_Institution
    Neurosci. et Modelisation, UPMC, Paris, France
  • Volume
    2
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1053
  • Abstract
    A basic architecture inspired from dentate gyrus and CA3-CA1 hippocampal fields combines a spectral timing module and an association network learning event transitions. According to the type of input the system can learn and replay: purely temporal sequences of aperiodic events; place-field chains as building blocks of graphs and maps, by combining visual and path-integration inputs; imitated sequences of movements by combining optic flow and movement-related proprioceptive feedback. The model is part of a triptych featuring also place cell computation and planning. The integrated architecture is used as a control system for robot navigation, sequence learning, prediction and novelty detection
  • Keywords
    brain models; learning (artificial intelligence); mechanoception; mobile robots; neural nets; neurophysiology; path planning; physiological models; CA3-CA1 hippocampal fields; accumbens; aperiodic events; association network; dentate gyrus; event transitions; hippocampus; imitated movement sequences; movement-related proprioceptive feedback; novelty detection; optic flow; path-integration inputs; place cell computation; place-field chains; planning; prediction; prefrontal cortex; purely temporal sequences; robot navigation; sequence learning; spectral timing module; visual inputs; Animals; Biomedical optical imaging; Gaussian processes; Hippocampus; Image motion analysis; Lesions; Navigation; Optical feedback; Optical sensors; Timing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.939506
  • Filename
    939506