Model-based neural decoding of reaching movements: a maximum likelihood approach

A new paradigm for decoding reaching movements from the signals of an ensemble of individual neurons is presented. This new method not only provides a novel theoretical basis for the task, but also results in a significant decrease in the error of reconstructed hand trajectories. By using a model of movement as a foundation for the decoding system, we show that the number of neurons required for reconstruction of the trajectories of point-to-point reaching movements in two dimensions can be halved. Additionally, using the presented framework, other forms of neural information, specifically neural "plan" activity, can be integrated into the trajectory decoding process. The decoding paradigm presented is tested in simulation using a database of experimentally gathered center-out reaches and corresponding neural data generated from synthetic models.