Neural decoding of movements: From linear to nonlinear trajectory models.
Byron M Yu, John P Cunningham, Krishna V Shenoy and Maneesh Sahani
In: Neural Information Processing – ICONIP 2007, Proceedings, Part I (2008) Springer , pp. 586-595.

Abstract

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 statis- tics of the movements using nonlinear trajectory models, thereby yield- ing more accurate decoded trajectories, is enticing. However, nonlinear decoding usually carries a higher computational cost, which is an im- portant consideration in real-time settings. In this paper, we present techniques for nonlinear decoding employing modal Gaussian approxi- mations, expectatation propagation, and Gaussian quadrature. We com- pare their decoding accuracy versus computation time tradeoffs based on high-dimensional simulated neural spike counts.

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