Adaptive sparse logistic regression with application to neuronal plasticity analysis

We consider the problem of estimating the time-varying parameters of a sparse logistic regression model in an online setting. We introduce two adaptive filters based on proximal gradient algorithms for recursive estimation of the model parameters by maximizing an ℓ1-regularized version of the observation log-likelihood, as well as an efficient online procedure for computing statistical confidence intervals around the estimates. We evaluate the performance of the proposed algorithms through simulation studies as well as application to real spiking data from the ferret's primary auditory cortex during a series of auditory tasks.