Optimizing the learning rate for adaptive estimation of neural encoding models.

ABSTRACT: Closed-loop neurotechnologies often need to adaptively learn an encoding model that relates the neural activity to the brain state, and is used for brain state decoding. The speed and accuracy of adaptive learning algorithms are critically affected by the learning rate, which dictates how fast model parameters are updated based on new observations. Despite the importance of the learning rate, currently an analytical approach for its selection is largely lacking and existing signal processing methods vastly tune it empirically or heuristically. Here, we develop a novel analytical calibration algorithm for optimal selection of the learning rate in adaptive Bayesian filters. We formulate the problem through a fundamental trade-off that learning rate introduces between the steady-state error a

SUBMITTER: Hsieh HL 

PROVIDER: S-EPMC5993334 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature