Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex

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 nonparametric 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 nonGaussian 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.

• Type: Conference
• Year: 2001
• Where: NIPS
• Authors: Yun Gao, Michael J. Black, Elie Bienenstock, Shy Shoham, John P. Donoghue
• Keywords: Neural, Neural Firing, NIPS 2001, NIPS 2007, Probabilistic Inference Techniques