Michael J. Black: Research Projects

Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex

Gao, Y.
Black, M.
Bienenstock, E.
Shoham, S.
Donoghue, J.

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.

This work was supported by the Keck Foundation and by the National Institutes of Health under grants #R01 NS25074 and #N01-NS-9-2322 and by the National Science Foundation ITR Program award #0113679. We are very grateful to M. Serruya, M. Fellows, L. Paninski, and N. Hatsopoulos who provided the neural data and valuable insight.

Related Publications

Gao, Y., Black, M. J., Bienenstock, E., Shoham, S., Donoghue, J., Probabilistic inference of arm motion from neural activity in motor cortex, Advances in Neural Information Processing Systems 14, The MIT Press, 2002. (postscript), (pdf). Inferring hand motion from multi-cell recordings in motor cortex using a Kalman filter,
Wu, W., Black, M. J., Gao, Y., Bienenstock, E., Serruya, M., and Donoghue, J. P.,
to appear: SAB'02-Workshop on Motor Control in Humans and Robots: On the Interplay of Real Brains and Artificial Devices, August 10, 2002, Edinburgh, Scotland (UK).
(abstract), (postscript),. (pdf).

Gao, Y., Bienenstock, E., Black, M., Shoham, S., Serruya, M., Donoghue, J., Encoding/decoding of arm kinematics from simultaneously recorded MI neurons, Society for Neuroscience Abst., Vol. 27, Program No. 572.14 2001. (abstract)

Related Talks

The Man Who Mistook His Computer for a Hand: Neural Control of Robotic Devices. Invited talk, Center for Autonomous Systems, KTH, Stockholm.

The Machine Inside, Voyages of Discovery, Inauguration of Ruth J. Simmons, 18th President of Brown Univ., with E. Bienenstock, D. Sheinberg, and M. Serruya. Providence, RI, Oct. 2001.


This material is based upon work supported by the National Science Foundation under Grant No. 0113679.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.