Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140623
Title: Perception coordination network : a neuro framework for multimodal concept acquisition and binding
Authors: Xing, You-Lu
Shi, Xiao-Feng
Shen, Fu-Rao
Zhao, Jin-Xi
Pan, Jing-Xin
Tan, Ah-Hwee
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Xing, Y.-L., Shi, X.-F., Shen, F.-R., Zhao, J.-X., Pan, J.-X., & Tan, A.-H. (2019). Perception coordination network : a neuro framework for multimodal concept acquisition and binding. IEEE Transactions on Neural Networks and Learning Systems, 30(4), 1104-1118. doi:10.1109/tnnls.2018.2861680
Journal: IEEE Transactions on Neural Networks and Learning Systems
Abstract: To simulate the concept acquisition and binding of different senses in the brain, a biologically inspired neural network model named perception coordination network (PCN) is proposed. It is a hierarchical structure, which is functionally divided into the primary sensory area (PSA), the primary sensory association area (SAA), and the higher order association area (HAA). The PSA contains feature neurons which respond to many elementary features, e.g., colors, shapes, syllables, and basic flavors. The SAA contains primary concept neurons which combine the elementary features in the PSA to represent unimodal concept of objects, e.g., the image of an apple, the Chinese word "[píng guǒ]" which names the apple, and the taste of the apple. The HAA contains associated neurons which connect the primary concept neurons of several PSA, e.g., connects the image, the taste, and the name of an apple. It means that the associated neurons have a multimodal response mode. Therefore, this area executes multisensory integration. PCN is an online incremental learning system, it is able to continuously acquire and bind multimodality concepts in an online way. The experimental results suggest that PCN is able to handle the multimodal concept acquisition and binding effectively.
URI: https://hdl.handle.net/10356/140623
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2018.2861680
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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