Yun Gao, Michael Black, Elie Bienenstock, Shy Shoham, John Donoghue
Statistical learning and probabilistic inference techniques are used to in- fer the hand position of a subject from multi-electrode recordings of neu- ral 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.