Probabilistically Modeling and Decoding Neural Population Activity in Motor Cortex

This paper introduces and summarizes recent work on probabilistic models of motor cortical activity and methods for inferring, or decoding, hand movements from this activity. A simple generalization of previous encoding models is presented in which neural firing rates are represented as a linear function of hand movements. A Bayesian approach is taken to exploit this generative model of firing rates for the purpose of inferring hand kinematics. In particular, we consider approximations of the encoding problem that allow efficient inference of hand movement using a Kalman filter. Decoding results are presented and the use of these methods for neural prosthetic cursor control is discussed.