<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "A Neural Model of Corticocerebellar Interactions During Attentive Imitation And Predictive Learning Of Sequential Handwriting Movements"^^ . "\nA NEURAL MODEL OF CORTICOCEREBELLAR INTERACTIONS DURING\nATTENTIVE IMITATION AND PREDICTIVE LEARNING OF SEQUENTIAL\nHANDWRITING MOVEMENTS\nRAINER WALTER PAINE\nBoston University Graduate School of Arts and Sciences, 2002\nMajor Professor: Stephen Grossberg, Wang Professor of Cognitive and Neural Systems\nABSTRACT\nMuch sensory-motor behavior develops through imitation, as during the learning of\nhandwriting by children. Such complex sequential acts are broken down into distinct\nmotor control synergies, or muscle groups, whose activities overlap in time to generate\ncontinuous, curved movements that obey an inverse relation between curvature and speed.\nHow are such complex movements learned through attentive imitation? Novel movements\nmay be made as a series of distinct segments, but a practiced movement can be made\nsmoothly, with a continuous, often bell-shaped, velocity profile. How does learning of\ncomplex movements transform reactive imitation into predictive, automatic performance?\n\nA neural model is developed which suggests how parietal and motor cortical mechanisms,\nsuch as difference vector encoding, interact with adaptively-timed, predictive cerebellar\nlearning during movement imitation and predictive performance. To initiate\nmovement, visual attention shifts along the shape to be imitated and generates vector\nmovement using motor cortical cells. During such an imitative movement, cerebellar\nPurkinje cells with a spectrum of delayed response profiles sample and learn the changing\ndirectional information and, in turn, send that learned information back to the cortex and\neventually to the muscle synergies involved. If the imitative movement deviates from an\nattentional focus around a shape to be imitated, the visual system shifts attention, and may\nsaccade, back to the shape, thereby providing corrective directional information to the arm\nmovement system. This imitative movement cycle repeats until the corticocerebellar system\ncan accurately drive the movement based on memory alone.\nA cortical working memory buffer transiently stores the cerebellar output and releases\nit at a variable rate, allowing speed scaling of learned movements which is limited by the\nrate of cerebellar memory readout. Movements can be learned at variable speeds if the\ndensity of the spectrum of delayed cellular responses in the cerebellum varies with speed.\nLearning at slower speeds facilitates learning at faster speeds. Size can be varied after\nlearning while keeping the movement duration constant. Context effects arise from the\noverlap of cerebellar memory outputs. The model is used to simulate key psychophysical\nand neural data about learning to make curved movements.\n"^^ . "2002" . . . "Boston University"^^ . . . "Cognitive and Neural Systems, Boston University"^^ . . . . . . . . . . "Rainer W."^^ . "Paine"^^ . "Rainer W. Paine"^^ . . . . . . "A Neural Model of Corticocerebellar Interactions During Attentive Imitation And Predictive Learning Of Sequential Handwriting Movements (PDF)"^^ . . . . . . . . . "RPPreface.pdf"^^ . . . "RPText.pdf"^^ . . . "RPTitlepage.pdf"^^ . . . "A Neural Model of Corticocerebellar Interactions During Attentive Imitation And Predictive Learning Of Sequential Handwriting Movements (Indexer Terms)"^^ . . . . . . "indexcodes.txt"^^ . . "HTML Summary of #2287 \n\nA Neural Model of Corticocerebellar Interactions During Attentive Imitation And Predictive Learning Of Sequential Handwriting Movements\n\n" . "text/html" . . . "Machine Learning" . . . "Neural Nets" . . . "Neural Modelling" . .