Rotation-Invariant Pattern Recognition: A Procedure Slightly Inspired on Olfactory System and Based on Kohonen Network

摘要

A computational scheme for rotation-invariant pattern recognition based on Kohonen neural network is developed. This scheme is slightly inspired on the vertebrate olfactory system, and its goal is to recognize spatiotemporal patterns produced in a two-dimensional cellular automaton that would represent the olfactory bulb activity when submitted to odor stimuli. The recognition occurs through a multi-layer Kohonen network that would represent the olfactory cortex. The recognition is invariant to rotations of the patterns, even when a noise lower than 1% is added.