Removing independent noise in systems neuroscience data using DeepInterpolation.

Abstract

Progress in many scientific disciplines is hindered by the presence of independent noise. Technologies for measuring neural activity—calcium imaging, extracellular electrophysiology, and fMRI—operate in domains in which independent noise (shot noise and/or thermal noise) can overwhelm physiological signals. Here, we introduce DeepInterpolation, a general-purpose denoising algorithm that trains a spatiotemporal nonlinear interpolation model using only raw noisy samples. Applying DeepInterpolation to two-photon calcium imaging data yielded up to 6 times more neuronal segments than in raw data with a 15-fold increase in single-pixel SNR, uncovering single-trial network dynamics that were previously obscured by noise. Extracellular electrophysiology recordings processed with DeepInterpolation contained 25% more high-quality spiking units than in raw data, while on fMRI datasets, DeepInterpolation produced a 1.6-fold increase in the SNR of individual voxels. Denoising was attained without sacrificing spatial or temporal resolution, and without access to ground truth training data. We anticipate that DeepInterpolation will provide similar benefits in other domains in which independent noise contaminates spatiotemporally structured datasets.