Self-Organizing Body Schema for Motor Planning

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Abstract
This article presents a distributed computational architecture for the motor planning functions of the posterior parietal cortex, which is organized as a computational map and combines a paradigm of self-organization (for building robust and coherent maps of the different motor spaces) with an attractor dynamics (for run-time integration of task constraints). The model, named SO-BoS (self-organizing body-schema), is illustrated with simple simulation results.