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Identifying signal and noise structure in neural population activity with Gaussian process factor models
Stephen Keeley · Mikio Aoi · Yiyi Yu · Spencer Smith · Jonathan Pillow

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1340

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 considerednoise''. 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 in monkey V1, 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.

Author Information

Stephen Keeley (Princeton University)
Mikio Aoi (Princeton University)
Yiyi Yu (UNC)
Spencer Smith (UC Santa Barbara)
Spencer Smith

Spencer LaVere Smith earned his BS in physics and mathematics (U Iowa), his Ph.D in neuroscience and neuroengineering (UCLA), and did postdoctoral work on multiphoton imaging and in vivo dendritic electrophysiology (Univ. College. London). Half of his lab builds technology for measuring and manipulating neural activity. The other half of the lab uses the technology to perform experiments and gain mechanistic insights into neural circuitry. The lab (slslab.org, labrigger.com) has developed novel multiphoton imaging instrumentation to measure neuronal activity with subcellular resolution across multiple brain areas simultaneously. His awards include a PECASE (2019) and a McKnight Technological Innovation Award (2015).

Jonathan Pillow (Princeton University)

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