The Community for Technology Leaders
Neural Networks, IEEE - INNS - ENNS International Joint Conference on (2009)
Atlanta, Ga, USA
June 14, 2009 to June 19, 2009
ISBN: 978-1-4244-3548-7
pp: 3111-3117
M.J. Healy , Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, USA
M.L. Bernard , Sandia National Laboratories, Albuquerque, New Mexico, USA
S.J. Verzi , Sandia National Laboratories, Albuquerque, New Mexico, USA
J.D. Morrow , Sandia National Laboratories, Albuquerque, New Mexico, USA
T.P. Caudell , Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, USA
S.E. Taylor , Sandia National Laboratories, Albuquerque, New Mexico, USA
C.M. Vineyard , Sandia National Laboratories, Albuquerque, New Mexico, USA
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.
INDEX TERMS
CITATION
M.J. Healy, M.L. Bernard, S.J. Verzi, J.D. Morrow, T.P. Caudell, S.E. Taylor, C.M. Vineyard, "Temporal semantics: An Adaptive Resonance Theory approach", Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 00, no. , pp. 3111-3117, 2009, doi:10.1109/IJCNN.2009.5178925
79 ms
(Ver 3.3 (11022016))