Cortical maps for motor planning

A distributed computational architecture for the motor planning functions is explored. It combines a paradigm of self-organization (for building robust and coherent maps of the different motor spaces) with relaxation dynamics (for run-time incorporation of task constraints). The model, named SOBoS (Self-Organizing Body Schema), is illustrated with simulation results. Unlike other attempts to use self-organizing techniques for learning sensory-motor mappings, SOBoS can overcome the redundancy problem by learning, during exploratory movements, the direct motor space transformations (which are always well-defined) instead of the inverse ones (which are ill-defined for redundant systems) and by performing, during planning, a relaxation in a potential landscape determined by a combination of multiple task-dependent constraints. The model can initiate actual movements, by supplying the cerebral motor cortex and the cerebellar cortex with the necessary planning patterns, or can support mental simulations with an accurate reproduction of spatial and temporal patterns