RT Journal Article
SR Electronic
T1 Identifying signal and noise structure in neural population activity with Gaussian process factor models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.23.217984
DO 10.1101/2020.07.23.217984
A1 Stephen L. Keeley
A1 Mikio C. Aoi
A1 Yiyi Yu
A1 Spencer L. Smith
A1 Jonathan W. Pillow
YR 2020
UL http://biorxiv.org/content/early/2020/07/24/2020.07.23.217984.abstract
AB Neural datasets often contain measurements of neural activity across multiple trials of a repeated stimulus or behavior. An important problem in the analysis of such datasets is to characterize systematic aspects of neural activity that carry information about the repeated stimulus or behavior of interest, which can be considered “signal”, and to separate them from the trial-to-trial fluctuations in activity that are not time-locked to the stimulus, which for purposes of such analyses can be considered “noise”. Gaussian Process factor models provide a powerful tool for identifying shared structure in high-dimensional neural data. However, they have not yet been adapted to the problem of characterizing signal and noise in multi-trial datasets. 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. To learn the parameters of our model, we introduce a Fourier-domain black box variational inference method that quickly identifies smooth latent structure. The resulting model reliably uncovers latent signal and trial-to-trial noise-related fluctuations in large-scale recordings. We use this model to show that predominantly, noise fluctuations perturb neural activity within a subspace orthogonal to signal activity, suggesting that trial-by-trial noise does not interfere with signal representations. Finally, we extend the model to capture statistical dependencies across brain regions in multi-region data. We show that in mouse visual cortex, models with shared noise across brain regions out-perform models with independent per-region noise.Competing Interest StatementThe authors have declared no competing interest.