Optimizing the learning rate for adaptive estimation of neural encoding models
Fig 4
Parameter adaptation profiles confirm the accuracy of the calibration algorithm with continuous signals.
(A–C) show sample adaptation profiles of the model parameters ψt|t for different learning rates s in ascending order. For each learning rate, the estimated parameters are within the analytically-computed 95% confidence bounds by the calibration algorithm about 96% of the time, demonstrating the accuracy of the calibration algorithm.