Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex

Statistical learning and probabilistic inference techniques are used to infer the hand position of a subject from multi-electrode recordings of neural activity in motor cortex. First, an array of electrodes provides training data of neural firing conditioned on hand kinematics. We learn a non-parametric representation of this firing activity using a Bayesian model and rigorously compare it with previous models using cross-validation. Second, we infer a posterior probability distribution over hand motion conditioned on a sequence of neural test data using Bayesian inference. The learned firing models of multiple cells are used to define a non-Gaussian likelihood term which is combined with a prior probability for the kinematics. A particle filtering method is used to represent, update, and propagate the posterior distribution over time. The approach is compared with traditional linear filtering methods; the results suggest that it may be appropriate for neural prosthetic applications.

[1]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[2]  A B Schwartz,et al.  Motor cortical representation of speed and direction during reaching. , 1999, Journal of neurophysiology.

[3]  A. P. Georgopoulos,et al.  Neuronal population coding of movement direction. , 1986, Science.

[4]  Robert D. Nowak,et al.  A statistical multiscale framework for Poisson inverse problems , 2000, IEEE Trans. Inf. Theory.

[5]  Stuart Geman,et al.  Statistical methods for tomographic image reconstruction , 1987 .

[6]  Demetri Terzopoulos,et al.  Regularization of Inverse Visual Problems Involving Discontinuities , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Jerald D. Kralik,et al.  Real-time prediction of hand trajectory by ensembles of cortical neurons in primates , 2000, Nature.

[8]  L. Paninski,et al.  Information about movement direction obtained from synchronous activity of motor cortical neurons. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[9]  J. Donoghue,et al.  Neuronal Interactions Improve Cortical Population Coding of Movement Direction , 1999, The Journal of Neuroscience.

[10]  T. Ebner,et al.  Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons. , 1995, Journal of neurophysiology.

[11]  E N Brown,et al.  A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells , 1998, The Journal of Neuroscience.