Transfer Learning of Fuzzy Spatio-Temporal Rules in a Brain-Inspired Spiking Neural Network Architecture: A Case Study on Spatio-Temporal Brain Data-Reference-Cited by-同舟云学术

Transfer Learning of Fuzzy Spatio-Temporal Rules in a Brain-Inspired Spiking Neural Network Architecture: A Case Study on Spatio-Temporal Brain Data

Published:2023-12 Issue:12 Volume:31 Page:4542-4552
ISSN:1063-6706
Container-title:IEEE Transactions on Fuzzy Systems
language:
Short-container-title:IEEE Trans. Fuzzy Syst.

Author:

Kasabov Nikola K.¹^ORCID,Tan Yongyao¹,Doborjeh Maryam¹,Tu Enmei²^ORCID,Yang Jie²^ORCID,Goh Wilson³,Lee Jimmy³^ORCID

Affiliation:

1. School of Engineering, Computer, and Mathematical Science, Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand

2. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tiong University, Shanghai, China

3. Nanyang Technological University, Singapore

Funder

Data Science Fund MBIE-Singapore

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/91/10336246/10175605.pdf?arnumber=10175605

Reference54 articles.

1. Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition

2. Upper limb complex movements decoding from pre-movement EEG signals using wavelet common spatial patterns

3. Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System

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