TIMING AND CHUNKING IN PROCESSING TEMPORAL-ORDER

A computational framework of learning, recognition and reproduction of temporal sequences are provided, based on an interference theory of forgetting in short-term memory (STM), modeled as a network of neural units with mutual inhibition. The STM model provides information for recognition and reproduction of arbitrary temporal sequences. Sequences are acquired by a new learning rule, the attentional learning rule, which combines Hebbian learning and a normalization rule with sequential system activation. Acquired sequences can be recognized without being affected by speed of presentation or certain distortions in symbol form. Different layers of the STM model can be naturally constructed in a feedforward manner to recognize hierarchical sequences, significantly expanding the model's capability in a way similar to human information chunking. A model of sequence reproduction is presented that consists of two reciprocally connected networks, one of which behaves as a sequence recognizer. Reproduction of complex sequences can maintain interval lengths of sequence components, and vary the overall speed. A mechanism of degree self-organization based on a global inhibitor is proposed for the model to learn required context lengths in order to disambiguate associations in complex sequence reproduction. Certain implications of the model are discussed at the end of the paper.