Recursive bayesian decoding of motor cortical signals by particle filtering

The population vector (PV) algorithm and optimal linear estimation (OLE) have been used to reconstruct movement by combining signals from multiple neurons in the motor cortex. While these linear methods are effective, recursive Bayesian decoding schemes, which are nonlinear, can be more powerful whe...

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Vydáno v:Journal of neurophysiology Ročník 91; číslo 4; s. 1899
Hlavní autoři: Brockwell, A E, Rojas, A L, Kass, R E
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.04.2004
Témata:
Algorithms
Animals
Bayes Theorem
Computer Simulation
Hand - physiology
Haplorhini
Models, Neurological
Motor Cortex - cytology
Motor Cortex - physiology
Movement
Neurons - physiology
Signal Detection, Psychological - physiology
Time Factors
ISSN:0022-3077
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Shrnutí:The population vector (PV) algorithm and optimal linear estimation (OLE) have been used to reconstruct movement by combining signals from multiple neurons in the motor cortex. While these linear methods are effective, recursive Bayesian decoding schemes, which are nonlinear, can be more powerful when probability model assumptions are satisfied. We have implemented a recursive Bayesian algorithm for reconstructing hand movement from neurons in the motor cortex. The algorithm uses a recently developed numerical method known as "particle filtering" and follows the same general strategy as that used by Brown et al. to reconstruct the path of a foraging rat from hippocampal place cells. We investigated the method in a numerical simulation study in which neural firing rate was assumed to be positive, but otherwise a linear function of movement velocity, and preferred directions were not uniformly distributed. In terms of mean-squared error, the approach was approximately 10 times more efficient than the PV algorithm and 5 times more efficient than OLE. Thus use of recursive Bayesian decoding can achieve the accuracy of the PV algorithm (or OLE) with approximately 10 times (or 5 times) fewer neurons. The method was also used to reconstruct hand movement in an ellipse-drawing task from 258 cells in the ventral premotor cortex. Recursive Bayesian decoding was again more efficient than the PV and OLE methods, by factors of roughly seven and three, respectively.
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ISSN:0022-3077
DOI:10.1152/jn.00438.2003