Neural Decoding Using a Parallel Sequential Monte Carlo Method on Point Processes with Ensemble Effect

Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding perfor...

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Bibliographic Details
Published in:BioMed research international Vol. 2014; no. 2014; pp. 1 - 11
Main Authors: Wang, Yiwen, Xu, Kai, Wang, Fang, Liao, Yuxi, Zhang, Qiaosheng, Li, Hongbao, Zheng, Xiaoxiang
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Puplishing Corporation 01.01.2014
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Subjects:
Accuracy
Algorithms
Animals
Biomechanical Phenomena
Biomedical engineering
Computational biology
Computer simulation
Construction
Decoding
Humans
Innovations
Kalman filters
Kinematics
Mathematical models
Models, Neurological
Models, Theoretical
Monte Carlo Method
Monte Carlo methods
Neural circuitry
Neurons
Observations
Psychomotor Performance
Roots
Science
Signal processing
Studies
Trains
Tuning
ISSN:2314-6133, 2314-6141, 2314-6141
Online Access:Get full text
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Summary:Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.
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Academic Editor: Ting Zhao
ISSN:2314-6133
2314-6141
2314-6141
DOI:10.1155/2014/685492