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Computational & functional neuroimaging methods for investigating sensorimotor adaptation learning| old_uid | 1558 |
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| title | Computational & functional neuroimaging methods for investigating sensorimotor adaptation learning |
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| start_date | 2006/09/25 |
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| schedule | 12h |
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| online | no |
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| summary | In this talk I will present recent computational methods used to study how the brain learns internal representations of sensorimotor transformations for reaching or new tool use. These methods (based on neural network modeling and independent component analysis applied to EEG/MEG data), provide insights on how the task load may be distributed across the visuomotor networks of the brain. They also allow the identification of artifactual and task-related neural components. ICA-based analysis of task components in an EEG study suggests two types of neural networks for adaptation to kinematic distortions: a modifiable but well defined visuomotor network for visuomotor transformations that is active across baseline and exposure trials irrespective of task condition, and a set of components representing transient visuomotor networks recruited during exposure trials only as subjects adapt to the kinematic perturbation. |
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| responsibles | Baillet |
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