Toward a Cognitive Sequence Learner: Hierarchy, Self-Organization, and Top-down Bottom-up Interaction

This paper introduces a hierarchical learning architecture that grows online an adaptive problem representation from scratch. The representation extracts frequent perceptual patterns representing them in a layered hierarchy where neural activity in higher layers is initiated bottom-up by firing neurons in lower layers and, vice versa, firing neurons in higher layers predispose activity and provide reinforcement feedback to neurons in lower layers. The structure evolves by a mixture of reinforcement learning and a genetic algorithm. Learning is biased towards extracting frequently recurring sequences in an input stream. We evaluate the architecture on a text document, which is presented iteratively—character by character—to the system. The results show that the proposed system reliably evolves representations of most frequent characters, syllables, and words in the document. We also confirm that top-down influences bias the evolution of syllable and character representations.