Inferring neural firing rates from spike trains using Gaussian processes.
John P Cunningham, Byron M Yu, Krishna V Shenoy and Maneesh Sahani
Advances in Neural Information Processing Systems
, Cambridge, USA
Neural spike trains present challenges to analytical efforts due to their noisy,
spiking nature. Many studies of neuroscientific and neural prosthetic importance
rely on a smoothed, denoised estimate of 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 test the
performance of the method on simulated data and experimentally gathered neural
spike trains, and we demonstrate improvements over conventional estimators.