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Probabilistic inference of hand motion from neural activity in motor cortex

2002

Conference Paper

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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 train- ing 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 com- pared with traditional linear filtering methods; the results suggest that it may be appropriate for neural prosthetic applications.

Author(s): Gao, Y. and Black, M. J. and Bienenstock, E. and Shoham, S. and Donoghue, J.
Book Title: Advances in Neural Information Processing Systems 14
Pages: 221-228
Year: 2002
Publisher: MIT Press

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Links: pdf

BibTex

@inproceedings{Black:ANIPS:2002,
  title = {Probabilistic inference of hand motion from neural activity in motor cortex},
  author = {Gao, Y. and Black, M. J. and Bienenstock, E. and Shoham, S. and Donoghue, J.},
  booktitle = {Advances in Neural Information Processing Systems 14},
  pages = {221-228},
  publisher = {MIT Press},
  year = {2002}
}