Paper Info
Title
Unravelling The Spatio-Temporal Neurodynamics Of Rhythm Encoding-Reproduction Networks By A Novel Fmri Autoencoder
Abstract
Visualization of how the external stimuli are processed dynamically in the brain would help understanding the neural mechanisms of functional segregation and integration. The present study proposed a novel temporal autoencoder to estimate the neurodynamics of functional networks involved in rhythm encoding and reproduction. A fully-connected two-layer autoencoder was proposed to estimate the temporal dynamics in functional magnetic resonance image recordings. By minimizing the reconstruction error between the predicted next time sample and the corresponding ground truth next time sample, the system was trained to extract spatial patterns of functional network dynamics without any supervision effort. The results showed that the proposed model was able to extract the spatial patterns of task-related functional dynamics as well as the interactions between them. Our findings suggest that artificial neural networks would provide a useful tool to resolve temporal dynamics of neural processing in the human brain.
Year
DOI
Venue
2019
10.1109/ner.2019.8716917
2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)
Field
DocType
ISSN
Autoencoder,Computer science,Speech recognition,Artificial intelligence,Rhythm,Machine learning,Encoding (memory)
Conference
1948-3546
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Chia-Hsiang Kao100.34
Ching-Ju Yang200.34
Li-Kai Cheng300.68
Hsin-Yen Yu400.68
Yong-Sheng Chen531430.12
Jen-Chuen Hsieh618827.76
Li-Fen Chen7183.31