Biomimetic sensory abstraction using hierarchical quilted self-organizing maps
Jeffrey W. Miller, P. Lommel · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
We present an approach for abstracting invariant classifications of spatiotemporal patterns presented in a high-dimensionality input stream, and apply an early proof-of-concept to shift and scale invariant shape recognition. A model called Hierarchical Quilted Self-Organizing Map (HQSOM) is developed, using recurrent self-organizing maps (RSOM) arranged in a pyramidal hierarchy, attempting to mimic the parallel/hierarchical pattern of isocortical processing in the brain. The results of experiments are presented in which the algorithm learns to classify multiple shapes, invariant to shift and scale transformations, in a very small (7×7 pixel) field of view.