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A Neural Model of Corticocerebellar Interactions During Attentive Imitation And Predictive Learning Of Sequential Handwriting Movements

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Document pages: 142 pages

Abstract: A NEURAL MODEL OF CORTICOCEREBELLAR INTERACTIONS DURINGATTENTIVE IMITATION AND PREDICTIVE LEARNING OF SEQUENTIALHANDWRITING MOVEMENTSRAINER WALTER PAINEBoston University Graduate School of Arts and Sciences, 2002Major Professor: Stephen Grossberg, Wang Professor of Cognitive and Neural SystemsABSTRACTMuch sensory-motor behavior develops through imitation, as during the learning ofhandwriting by children. Such complex sequential acts are broken down into distinctmotor control synergies, or muscle groups, whose activities overlap in time to generatecontinuous, curved movements that obey an inverse relation between curvature and speed.How are such complex movements learned through attentive imitation? Novel movementsmay be made as a series of distinct segments, but a practiced movement can be madesmoothly, with a continuous, often bell-shaped, velocity profile. How does learning ofcomplex movements transform reactive imitation into predictive, automatic performance?A neural model is developed which suggests how parietal and motor cortical mechanisms,such as difference vector encoding, interact with adaptively-timed, predictive cerebellarlearning during movement imitation and predictive performance. To initiatemovement, visual attention shifts along the shape to be imitated and generates vectormovement using motor cortical cells. During such an imitative movement, cerebellarPurkinje cells with a spectrum of delayed response profiles sample and learn the changingdirectional information and, in turn, send that learned information back to the cortex andeventually to the muscle synergies involved. If the imitative movement deviates from anattentional focus around a shape to be imitated, the visual system shifts attention, and maysaccade, back to the shape, thereby providing corrective directional information to the armmovement system. This imitative movement cycle repeats until the corticocerebellar systemcan accurately drive the movement based on memory alone.A cortical working memory buffer transiently stores the cerebellar output and releasesit at a variable rate, allowing speed scaling of learned movements which is limited by therate of cerebellar memory readout. Movements can be learned at variable speeds if thedensity of the spectrum of delayed cellular responses in the cerebellum varies with speed.Learning at slower speeds facilitates learning at faster speeds. Size can be varied afterlearning while keeping the movement duration constant. Context effects arise from theoverlap of cerebellar memory outputs. The model is used to simulate key psychophysicaland neural data about learning to make curved movements.

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