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
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"
Conference_Titel :
Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2015 Joint IEEE International Conference on
DOI :
10.1109/DEVLRN.2015.7346150