no code implementations • ICML 2020 • Stephen Keeley, David Zoltowski, Jonathan Pillow, Spencer Smith, Yiyi Yu
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.
no code implementations • NeurIPS 2020 • Stephen Keeley, Mikio Aoi, Yiyi Yu, Spencer Smith, Jonathan W. Pillow
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.