Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99374
Title: Neural network structure for spatio-temporal long-term memory
Authors: Nguyen, Vu Anh
Goh, Wooi Boon
Jachyra, Daniel
Starzyk, Janusz A.
Keywords: DRNTU::Engineering::Computer science and engineering::Computer systems organization::Computer-communication networks
Issue Date: 2012
Source: Nguyen, V. A., Starzyk, J. A., Goh, W. B., & Jachyra, D. (2012). Neural network structure for spatio-temporal long-term memory. IEEE transactions on neural networks and learning systems, 23(6), 971-983.
Series/Report no.: IEEE transactions on neural networks and learning systems
Abstract: This paper proposes a neural network structure for spatio-temporal learning and recognition inspired by the long-term memory (LTM) model of the human cortex. Our structure is able to process real-valued and multidimensional sequences. This capability is attained by addressing three critical problems in sequential learning, namely the error tolerance, the significance of sequence elements and memory forgetting. We demonstrate the potential of the framework with a series of synthetic simulations and the Australian sign language (ASL) dataset. Results show that our LTM model is robust to different types of distortions. Second, our LTM model outperforms other sequential processing models in a classification task for the ASL dataset.
URI: https://hdl.handle.net/10356/99374
http://hdl.handle.net/10220/13514
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2012.2191419
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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