Utilize este identificador para referenciar este registo: http://hdl.handle.net/1822/33386

TítuloLearning a musical sequence by observation : a robotics implementation of a dynamic neural field model
Autor(es)Ferreira, Flora
Erlhagen, Wolfram
Sousa, Emanuel
Louro, Luís
Bicho, E.
Palavras-chaveDynamic field model
Sequence learning
Musical sequence
Time intervals
Error correction
Working memory
Action planning
Resumo(s)We tested in a robotics experiment a dynamic neural field model for learning a precisely timed musical sequence. Based on neuro-plausible processing mechanisms, 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. Dynamic neural fields provide a rigorous theoretical framework to analyze and implement the necessary neural computations that bridge gaps between sensation and action in order to mediate working memory, action planing, and decision making. The robot first memorizes a short musical sequence performed by a human teacher by watching color coded keys on a screen, and then tries to execute the piece of music on a keyboard from memory without any external cues. The experimental results show that the robot is able to correct in very few demonstration-execution cycles initial sequencing and timing errors.
Versão da editorahttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6982973
Arbitragem científicayes
Aparece nas coleções:CAlg - Artigos em livros de atas/Papers in proceedings
CMAT - Comunicações com arbitragem/Communications with refereeing

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MusicalSequenceICDL2014.pdfpreprint1,9 MBAdobe PDFVer/Abrir

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