PMID15010499[0] Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering
 It seems that particle filtering is 35 times more efficient / accurate than optimal linear control, and 710 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 centerout 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 singleunit monkey recording ellipsetracing 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 singleunit recordings.
 appendix details the 'innovative methodology ;)
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