In freely moving rats, motor cortical recordings enabled the use of a closed loop system to replace paddle pressing for a directional task. In this system, firing rates were estimated from several (8-10) motor cortical neurons at several consecutive time points. These firing rates were concatenated to form a neural activity vector (NAV). The NAV was used as input to a previously trained support vector machine (SVM) classifier. The decision function value obtained from the SVM was then used to determine which relay should be activated to produce paddle pressing signals in the task. Animals were able to use this interface immediately and significant changes in neural activity arose in a single, 45 minute, experimental session. Neural data from several subjects was examined for changes from the calibration phase to the late cortically controlled phase. Detailed analysis shows that NAVs changed significantly from the calibration phase to the cortically controlled phase, furthermore, the decision function values arising from these NAVs changed in interesting ways. By examining which neurons and times (dimensions of the NAV) were selected by the SVM to have significant impact on the decision function value as well as which dimensions of the NAV changed significantly, a mechanism of adaptation begins to emerge in which the SVM properly assigns high importance to dimensions that easily predict the desired output, however, under closed loop control, the animal selects a small number of neurons (at most or all times) and chooses to make the firing rates more distinguishable. These differences offer insight into how the rats and their SVMs collaborated to create a useable interface.