Fig 7. Parameter adaptation profiles confirm the accuracy of the calibration algorithm with discrete spiking activity.

(A)–(C) show sample adaptation profiles of model parameters ϕ_t|t in a closed-loop BMI simulation under different learning rates r in ascending order. Increasing the learning rate increases the error covariance. Also, about 96% of the time, the parameter estimates at steady state are within the 95% confidence bounds computed by the calibration algorithm; this demonstrates that the calibration algorithm can closely approximate the error covariance and consequently the confidence bounds.