• DocumentCode
    3704748
  • Title

    A neurorobotic model of learning to shake a rattle

  • Author

    Forrest Yeh;Anne S. Warlaumont;YangQuan Chen;Timothy M. Shea;Brandon Stark

  • Author_Institution
    Bioengineering, University of California, Merced
  • fYear
    2015
  • Firstpage
    250
  • Lastpage
    251
  • Abstract
    Reward-modulated Hebbian learning is a biologically plausible neural learning mechanism that has been previously applied to a variety of learning tasks. For example, recent work used reward-modulated spike timing dependent plasticity (STDP) to help explain how infants learn to produce syllabic babbling [1]. This project attempts to extend this learning mechanism to a new domain of infant motor development, shaking a rattle. The experiment transduces neural spike trains to adjust frequency of sinusoidal movement around a robotic arm´s articulation point. Reinforcement given when the volume, defined as the root mean square (RMS) amplitude, of sound made by a rattle attached to the robot arm exceeded the mean RMS of recent trials.
  • Keywords
    "Neurons","Mathematical model","Robots","Reservoirs","Servomotors","Biological neural networks","Frequency control"
  • Publisher
    ieee
  • Conference_Titel
    Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2015 Joint IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/DEVLRN.2015.7346150
  • Filename
    7346150