Abstract
This paper proposes a divide-and-conquer strategy for designing brain machine interfaces. A nonlinear combination of competitively trained local linear models (experts) is used to identify the mapping from neuronal activity in cortical areas associated with arm movement to the hand position of a primate. The proposed architecture and the training algorithm are described in detail and numerical performance comparisons with alternative linear and nonlinear modeling approaches, including time-delay neural networks and recursive multilayer perceptrons, are presented. This new strategy allows training the local linear models using normalized LMS and using a relatively smaller nonlinear network to efficiently combine the predictions of the linear experts. This leads to savings in computational requirements, while the performance is still similar to a large fully nonlinear network.
Original language | English (US) |
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Pages (from-to) | 865-871 |
Number of pages | 7 |
Journal | Neural Networks |
Volume | 16 |
Issue number | 5-6 |
DOIs | |
State | Published - 2003 |
Keywords
- Brain machine interfaces
- Competitive learning
- Multiple local linear models
ASJC Scopus subject areas
- Artificial Intelligence
- Neuroscience(all)