{176} revision 1 modified: 02-05-2007 18:54 gmt |

PMID-15010499[0] Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering - It seems that particle filtering is
**3-5**times more efficient / accurate than optimal linear control, and**7-10**times more efficient than the population vector method. - synthetic data: inhomogeneous poisson point process, 400 bins of 30ms width = 12 seconds, random walk model.
- monkey data: 258 neurons recorded in independent experiments in the ventral premotor cortex. monkey performed a 3D center-out task followed by an ellipse tracing task.
- Bayesian methods work optimally when their models/assumptions hold for the data being analyzed.
- Bayesian filters in the past were computationally inefficient; particle filtering was developed as a method to address this problem.
- tested the particle filter in a simulated study and a single-unit monkey recording ellipse-tracing experiment. (data from Rena and Schwartz 2003)
- there is a lot of math in the latter half of the paper describing their results. The tracings look really good, and I guess this is from the quality of the single-unit recordings.
- appendix details the 'innovative methodology ;)
____References____ |