Self-Organizing Neural Population Coding for improving robotic visuomotor coordination

Abstract

We present an extension of Kohonen's Self Organizing Map (SOM) algorithm called the Self Organizing Neural Population Coding (SONPC) algorithm. The algorithm adapts online the neural population encoding of sensory and motor coordinates of a robot according to the underlying data distribution. By allocating more neurons towards area of sensory or motor space which are more frequently visited, this representation improves the accuracy of a robot system on a visually guided reaching task. We also suggest a Mean Reflection method to solve the notorious border effect problem encountered with SOMs for the special case where the latent space and the data space dimensions are the same.