Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)
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.