Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning

Objective: In brain machine interfaces (BMIs), the functional mapping between neural activities and kinematic parameters varied over time owing to changes in neural recording conditions. The variability in neural recording conditions might result in unstable long-term decoding performance. Relevant...

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Bibliographic Details
Main Authors: Shih-Hung Yang, Han-Lin Wang, Yu-Chun Lo, Hsin-Yi Lai, Kuan-Yu Chen, Yu-Hao Lan, Ching-Chia Kao, Chin Chou, Sheng-Huang Lin, Jyun-We Huang, Ching-Fu Wang, Chao-Hung Kuo, You-Yin Chen
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-03-01
Series:Frontiers in Computational Neuroscience
Subjects:
brain machine interfaces
neural decoding
error feedback
evolutionary algorithm
recurrent neural network
Online Access:https://www.frontiersin.org/article/10.3389/fncom.2020.00022/full

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