Neural signals present challenges to analytical efforts due to their
noisy, spiking nature. Many studies of neuroscientific and neural
prosthetic importance rely on a smoothed, denoised neural signal
considered to be the spike train's underlying firing rate. Current
techniques to find time varying firing rates require ad hoc choices
of parameters, offer no confidence 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 firing rate
functions underlying single or multiple neural spike trains. We
simulate spike trains to test the performance of the method and
demonstrate significant average error improvement over standard
smoothing techniques.