Figure 6. The input perturbations caused significant performance drops without adaptation.

(A) displays the effect of different fractions of neural signals being lost on the performance of a nonadaptive neural decoder (Wiener classifier), relative to the average information available from the neural inputs (DU: 1000 simulations; PR: 700 simulations). The average mutual information (equation 5) between the neural signals and the two-target robot task (red boxes; DU: solid, PR: hollow) reflects the magnitude of the input perturbation caused by varying numbers of random neural signals being lost. Losing 50% of the inputs unsurprisingly resulted in a large input shift, with about half the available information similarly being lost by that point for each monkey. It is unsurprising that the cross-validation performance of a nonadaptive neural decoder (black circles; DU: solid, PR: hollow) that had been created prior to the perturbation (Figure 5) thus similarly approached chance performance for such large input losses (performance was quantified as classification accuracy for trials 11 to 30 with the perturbation occurring following trial 10). (B) shows how the RLBMI adapted (Figure 5) to large input perturbations (50% loss of neural signals and doubling of neural signals) during both closed loop experiments (signals lost: dark blue; new signals appear: dark red; 4 experiments) and the offline simulations (signals lost: light blue; new signals appear: light red; DU: 1000 simulations; PR: 700 simulations), resulting in higher performance than the nonadaptive Wiener classifier (hatched boxes, 1sided t-test, p<<.001).