Optimizing the learning rate for adaptive estimation of neural encoding models
Fig 6
The calibration algorithm accurately computes the steady-state error covariance for discrete spiking activity.
(A) The analytically-computed and the true steady-state error covariance as a function of the learning rate r. True values are found from closed-loop BMI simulations with a periodic center-out-and-back trajectory. The calibration algorithm analytically computes the covariance based on (19). The calibration algorithm closely approximates the steady-state error covariance as demonstrated by the closeness of the analytically-computed and true curves across a wide range of r. (B) Figure convention is the same as (A) except that all true values are computed in closed-loop BMI simulations with a non-periodic trajectory generated by selecting one of the eight targets randomly and uniformly in each trial. The calibration algorithm can again closely approximate the steady-state error covariance, demonstrating the generalizability of the approach to training datasets with varying state-evolution trajectories.