Wolfram Erlhagen

9 octobre 2015

Dept. Mathematics and Applications, University of Minho, Guimaraes, Portugal

Learning joint representations for order and timing of perceptual-motor sequences : A robotics implementation of a dynamic neural field approach

invité par Alexa Riehle


Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the theoretical framework of dynamic neural fields (DNF), I address the learning of neural representations that support the performance of precisely timed action sequences. The model implements the idea that order and relative timing of events are stored in an integrated representation whereas the onset of sequence production is controlled by a separate process. The model was tested in a robotics experiment in which the robot first memorizes a short musical sequence played by a human and subsequently tries to execute the sequence on a keyboard from memory without external cues. I show how feedback about executed actions might be used by the learning system to fine tune a joint memory representation which has been initially acquired by observation.

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