Neuron selection and visual training for population vector based cortical control.

We have developed a method for training animals to control artificial devices from cortical signals. In this report we describe a series of experiments designed to parameterize a cortical control algorithm without an animal having to move its arm. Instead, a highly motivated animal observes as the computer drives a cursor move towards a set of targets once each in a center-out task. From the neuronal activity recorded in this visual following task, we compute preferred directions for the neurons. We find that the quality of fit in this early set of trials is highly predictive of each neuron's contribution to the overall cortical control.