Gaussian Process Factor Analysis (GPFA) hasbeen broadly applied to the problem of identi-fying smooth, low-dimensional temporal struc-ture underlying large-scale neural recordings. However, spike trains are non-Gaussian, whichmotivates combining GPFA with discrete ob-servation models for binned spike count data. The drawback to this approach is that GPFApriors are not conjugate to count model like-lihoods, which makes inference challenging. Here we address this obstacle by introduc-ing a fast, approximate inference method fornon-conjugate GPFA models.
Here we address this shortcoming by proposing ``signal-noise'' Poisson-spiking Gaussian Process Factor Analysis (SNP-GPFA), a flexible latent variable model that resolves signal and noise latent structure in neural population spiking activity.
We apply the model to spike trains recorded from hippocampal place cells and show that it compares favorably to a variety of previous methods for latent structure discovery, including variational auto-encoder (VAE) based methods that parametrize the nonlinear mapping from latent space to spike rates with a deep neural network.