Perceiving Systems, Computer Vision

Probabilistic inference of hand motion from neural activity in motor cortex

2002

Conference Paper

ps


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},
  doi = {}
}