Top-down selective attention for robust perception of noisy and confusing patterns

Abstract

A neural network model is developed for the top-down selective attention (TDSA), which estimates the most probable sensory input signal based on previous knowledge and filters out irrelevant sensory signals for high-confidence perception of noisy and confusing signals. The TDSA is modeled as an adaptation process to minimize the attention error, which is implemented by the error backpropagation algorithm for the multilayer Perceptron classifiers. Sequential recognition of superimposed patterns one by one is also possible. The developed TDSA model is applied to the recognition tasks of two-pattern images, superimposed handwritten characters, and noise-corrupted speeches.