Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models

To date, the neural decoding of time-evolving physical state – for example, the path of a foraging rat or arm movements – has been largely carried out using linear trajectory models, primarily due to their computational efficiency. The possibility of better capturing the statistics of the movements using nonlinear trajectory models, thereby yielding more accurate decoded trajectories, is enticing. However, nonlinear decoding usually carries a higher computational cost, which is an important consideration in real-time settings. In this paper, we present techniques for nonlinear decoding employing modal Gaussian approximations, expectatation propagation, and Gaussian quadrature. We compare their decoding accuracy versus computation time tradeoffs based on high-dimensional simulated neural spike counts.