You are not authenticated, login.
text: sort by
tags: modified
type: chronology
[0] Brockwell AE, Rojas AL, Kass RE, Recursive bayesian decoding of motor cortical signals by particle filtering.J Neurophysiol 91:4, 1899-907 (2004 Apr)

hide / / print
ref: bookmark-0 tags: particle_filter unscented monte_carlo MCMC date: 12-11-2007 16:46 gmt revision:2 [1] [0] [head]


  • covers both the particle filter and the unscented kalman filter ... the unscented kalman filter is used as the proposal distribution.

hide / / print
ref: Brockwell-2004.04 tags: particle_filter Brockwell BMI 2004 wiener filter population_vector MCMC date: 02-05-2007 18:54 gmt revision:1 [0] [head]

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 ;)


hide / / print
ref: bookmark-0 tags: monte_carlo MCMC particle_filter probability bayes filtering biblography date: 0-0-2007 0:0 revision:0 [head]

http://www-sigproc.eng.cam.ac.uk/smc/papers.html -- sequential monte carlo methods. (bibliography)