Self Organization Computational Maps And Motor Control PDF Download

Self-organizing feature maps have become important neural modeling methods over the last several years. These methods have not only shown great potential in application fields such as motor control, pattern recognition, optimization, etc, but have also provided insights into how mammalian brains are organized. Most past work developing self-organizing features maps has focused on systems with a single map that is solely sensory in nature. This research develops and studies a model which has multiple self-organizing feature maps in a closed-loop control system, and that involves motor output as well as proprioceptive and/or visual sensory input. The model is driven by a simulated arm that moves in 3D space. By applying initial activations at randomly selected motor cortex regions, the neural network model spontaneously self-organizes, and demonstrates the appearance of multiple, reasonably stable motor and proprioceptive sensory maps and their interrelationships to each other. These cortical feature maps capture the mechanical constraints imposed by the model arm. They are aligned in a way consistent with a temporal correlation hypothesis: temporally correlated features usually cause their corresponding cortical map representations to be spatially correlated. Simulations of variations of the motor control model with visual inputs indicates the formation of visual input maps. These maps are also partiallyaligned with motor output maps, reflecting the degree of temporal correlations during training. The simultaneous presence of proprioceptive input causes the visual input maps to distinguish pairs of antagonist muscles and to be correlated with only one muscle in each pair. Moreover, some theoretical analysis with a simplified model gives insights into the nature of cortical feature maps and sheds light on the driving force behind map correlations. All of these results have provide more understanding about the organization of cortical feature maps, and how these maps might be used to achieve consistent motor commands based on sensory feedback.