Optimizing the learning rate for adaptive estimation of neural encoding models
Fig 5
The calibration algorithm generalizes to training datasets with non-periodic state trajectories.
Figure convention is the same as Fig 3. Here the true quantities are computed in closed-loop BMI simulations with a non-periodic trajectory generated by selecting targets randomly and uniformly. The analytically-computed error covariance and convergence times given by the calibration algorithm closely match their true values across a wide range of the learning rate s, showing that the calibration algorithm extends across training datasets with different state-evolution trajectories.