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: |
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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 = {} } |