| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
Innovative Methodology
1 Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213; 2 Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Submitted 26 January 2003; accepted in final form 30 October 2003
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 
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 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.
This article has been cited by other articles:
|
|
 |
|
  R. Natarajan, Q. J. M. Huys, P. Dayan, and R. S. Zemel Encoding and Decoding Spikes for Dynamic Stimuli Neural Comput., September 1, 2008; 20(9): 2325 - 2360. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. Czanner, U. T. Eden, S. Wirth, M. Yanike, W. A. Suzuki, and E. N. Brown Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics J Neurophysiol, May 1, 2008; 99(5): 2672 - 2693. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Srinivasan, U. T. Eden, S. K. Mitter, and E. N. Brown General-Purpose Filter Design for Neural Prosthetic Devices J Neurophysiol, October 1, 2007; 98(4): 2456 - 2475. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Shimazaki and S. Shinomoto A Method for Selecting the Bin Size of a Time Histogram Neural Comput., June 1, 2007; 19(6): 1503 - 1527. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. M. Yu, C. Kemere, G. Santhanam, A. Afshar, S. I. Ryu, T. H. Meng, M. Sahani, and K. V. Shenoy Mixture of Trajectory Models for Neural Decoding of Goal-Directed Movements J Neurophysiol, May 1, 2007; 97(5): 3763 - 3780. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. Ventura Spike Train Decoding Without Spike Sorting Neural Comput., April 1, 2007; 20(4): 923 - 963. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. Truccolo and J. P. Donoghue Nonparametric Modeling of Neural Point Processes via Stochastic Gradient Boosting Regression. Neural Comput., March 1, 2007; 19(3): 672 - 705. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Chi, W. Wu, Z. Haga, N. G. Hatsopoulos, and D. Margoliash Template-Based Spike Pattern Identification With Linear Convolution and Dynamic Time Warping J Neurophysiol, February 1, 2007; 97(2): 1221 - 1235. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. D. Sanger Bayesian Filtering of Myoelectric Signals J Neurophysiol, February 1, 2007; 97(2): 1839 - 1845. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Amarasingham, T.-L. Chen, S. Geman, M. T. Harrison, and D. L. Sheinberg Spike Count Reliability and the Poisson Hypothesis J. Neurosci., January 18, 2006; 26(3): 801 - 809. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. E. Kass, V. Ventura, and E. N. Brown Statistical Issues in the Analysis of Neuronal Data J Neurophysiol, July 1, 2005; 94(1): 8 - 25. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Shpigelman, Y. Singer, R. Paz, and E. Vaadia Spikernels: Predicting Arm Movements by Embedding Population Spike Rate Patterns in Inner-Product Spaces Neural Comput., March 1, 2005; 17(3): 671 - 690. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
W. Wu, Y. Gao, E. Bienenstock, J. P. Donoghue, and M. J. Black Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter Neural Comput., January 1, 2005; 18(1): 80 - 118. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Paninski, S. Shoham, M. R. Fellows, N. G. Hatsopoulos, and J. P. Donoghue Superlinear Population Encoding of Dynamic Hand Trajectory in Primary Motor Cortex J. Neurosci., September 29, 2004; 24(39): 8551 - 8561. [Abstract] [Full Text] [PDF]
|
|
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
