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In this work, we present the development of a neuro-inspired approach forcharacterizing sensorimotor relations in robotic systems. The proposed methodhas self-organizing and associative properties that enable it to autonomouslyobtain these relations without any prior knowledge of either the motor (e.g.mechanical structure) or perceptual (e.g. sensor calibration) models.Self-organizing topographic properties are used to build both sensory and motormaps, then the associative properties rule the stability and accuracy of theemerging connections between these maps. Compared to previous works, our methodintroduces a new varying density self-organizing map (VDSOM) that controls theconcentration of nodes in regions with large transformation errors withoutaffecting much the computational time. A distortion metric is measured toachieve a self-tuning sensorimotor model that adapts to changes in either motoror sensory models. The obtained sensorimotor maps prove to have less error thanconventional self-organizing methods and potential for further development.

A Self-Organizing Network with Varying Density Structure for Characterizing Sensorimotor Transformations in Robotic Systems