Wei Wu
Michael Black
Yun Gao
Elie Bienenstock
Mijail Serruya
John Donoghue

This paper develops a control-theoretic approach to the problem of decoding neural activity in motor cortex. Our goal is to infer the position and velocity of a subject's hand from the neural spiking activity of 25 cells simultaneously recorded in primary motor cortex. We propose to model the encoding and decoding of the neural data using a Kalman filter. Towards that end we specify a measurement model that assumes the firing rate of a cell within 50ms is a stochastic linear function of position, velocity, and acceleration of the hand. This model is learned from training data along with a system model that encodes how the hand moves. Experimental results show that the reconstructed trajectories are superior to those obtained by linear filtering. Additionally, the Kalman filter provides insight into the neural encoding of hand motion. For example, analysis of the measurement model suggests that, while the neural firing is closely related to the position and velocity of the hand, the acceleration is redundant. Furthermore, the Kalman filter framework is exploited to recover the optimal lag time between hand movement and neural firing.

This work was supported by the Keck Foundation, National Institutes of Health (#R01 NS25074 and #N01-NS-9-2322), DARPA (#MDA972-00-1-0026), and the National Science Foundation (ITR Program award #0113679).

Related Publications

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).
(postscript), (pdf).

Probabilistic inference of arm motion from neural activity in motor cortex,
Gao, Y., Black, M. J., Bienenstock, E., Shoham, S., Donoghue, J.,
Advances in Neural Information Processing Systems 14, The MIT Press, 2002.
(abstract), (postscript), (pdf).

Acknowledgement

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.

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