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Transforming visual signals into accurate 3-D reach plans| old_uid | 2502 |
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| title | Transforming visual signals into accurate 3-D reach plans |
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| start_date | 2007/03/22 |
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| schedule | 16h |
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| online | no |
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| summary | Most of our interactions with objects surrounding us are guided by vision. My research investigates how visual information about a desired reach target is translated into a geometrically accurate 3-D motor plan for the arm. To answer this question, I used three different approaches. First, I built a geometrical model which theoretically describes how extra-retinal eye and head position signals (or their absence) affect this visuomotor transformation. Specific predictions from this model were compared to behavioral human reaching performance, and revealed that the normally functioning brain uses extraretinal signals to transform visual signals into a reach plan. This model is also important in the analysis and interpretation of patient data where areas of the brain involved in these visuomotor transformation pathways are damaged. Second, I designed a simple feed-forward neural network to perform this visuomotor transformation. A detailed analysis of how this artificial neural network solves the visuomotor transformation shows how the brain could potentially perform this transformation and predicts which neural properties to look for in the involved brain regions. Finally, I identified neural structures as well as the timing underlying the visuomotor transformation for reaching in humans using functional brain imaging (magneto-encephalography). Taken together, these studies show why we need an explicit visuomotor transformation, how this could be achieved by the brain, which brain structures are involved in the different aspects and how they dynamically perform this visuomotor transformation from visual input to the motor output. |
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| responsibles | Farnè |
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