Optimizing the learning rate for adaptive estimation of neural encoding models

Han-Lin Hsieh, Maryam M. Shanechi

Abstract: Author summary Closed-loop neurotechnologies for treatment of neurological disorders often require adaptively learning an encoding model to relate the neural activity to the brain state and decode this state. Fast and accurate adaptive learning is critically affected by the learning rate, a key variable in any adaptive algorithm. However, existing signal processing algorithms select the learning rate empirically or heuristically due to the lack of a principled approach for learning rate calibration. Here, we develop a novel analytical calibration algorithm to optimally select the learning rate. The learning rate introduces a trade-off between the steady-state error and the convergence time of the estimated model parameters. Our calibration algorithm can keep the steady-state parameter error smaller than a desired value while minimizing the convergence time, or keep the convergence time faster than a desired value while minimizing the error. Using extensive closed-loop simulations, we show that the calibration algorithm allows for fast learning of accurate encoding models, and consequently for fast convergence of decoder performance to high values for both discrete-valued spike recordings and continuous-valued recordings such as local field potentials. The calibration algorithm can achieve a predictable level of speed and accuracy in adaptive learning, with significant implications for neurotechnologies.