Learning Algorithm for the Recursive Pattern Recognition Model

Abstract

In this work, we incorporate a learning algorithm to the recursive pattern recognition model, based on the systematic functioning of the human neocortex presented in previous works. This algorithm has two mechanisms: the first, called Aprendizaje_nuevo, is used to learn new patterns and creates a new pattern recognition module in the model. The other, called Aprendizaje_por_refuerzo, is used to reinforce a pattern and adapts the module that represents the pattern to the changes in it. The algorithm is tested in various contexts (text and images) to analyze its capacities of learning and of recognition of the model. © 2016 Taylor & Francis.