Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes

Neural spike trains present challenges to analytical efforts due to their noisy, spiking nature. Many studies of neuroscienti(cid:2)c and neural prosthetic importance rely on a smoothed, denoised estimate of the spike train’s underlying (cid:2)ring rate. Current techniques to (cid:2)nd time-varying (cid:2)ring rates require ad hoc choices of parameters, offer no con(cid:2)dence intervals on their estimates, and can obscure potentially important single trial variability. We present a new method, based on a Gaussian Process prior, for inferring probabilistically optimal estimates of (cid:2)ring rate functions underlying single or multiple neural spike trains. We test the performance of the method on simulated data and experimentally gathered neural spike trains, and we demonstrate improvements over conventional estimators.