Temporal semantics: an adaptive resonance theory approach

Authors:
S. E. Taylor;M. L. Bernard;S. J. Verzi;J. D. Morrow;C. M. Vineyard;M. J. Healy;T. P. Caudell
Affiliations:
Sandia National Laboratories, Albuquerque, New Mexico and Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico;Sandia National Laboratories, Albuquerque, New Mexico;Sandia National Laboratories, Albuquerque, New Mexico;Sandia National Laboratories, Albuquerque, New Mexico;Sandia National Laboratories, Albuquerque, New Mexico and Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico;Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico;Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

Encoding sensor observations across time is a critical component in the ability to model cognitive processes. All biological cognitive systems receive sensory stimuli as continuous streams of observed data over time. Therefore, the perceptual grounding of all biological cognitive processing is in temporal semantic encodings, where the particular grounding semantics are sensor modalities. We introduce a technique that encodes temporal semantic data as temporally integrated patterns stored in Adaptive Resonance Theory (ART) modules.