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bioRxiv · 10.1101/2020.10.15.341602

Removing independent noise in systems neuroscience data using DeepInterpolation

Lecoq, J.·Oliver, M.·Siegle, J. H.·Orlova, N.·Koch, C.
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Abstract

Progress in nearly every scientific discipline is hindered by the presence of independent noise in spatiotemporally structured datasets. Three widespread technologies for measuring neural activity--calcium imaging, extracellular electrophysiology, and fMRI--all operate in domains in which shot noise and/or thermal noise deteriorate the quality of measured physiological signals. Current denoising approaches sacrifice spatial and/or temporal resolution to increase the Signal-to-Noise Ratio of weak neuronal events, leading to missed opportunities for scientific discovery. Here, we introduce DeepInterpolation, a general-purpose denoising algorithm that trains a spatio-temporal nonlinear interpolation model using only noisy samples from the original raw data. Applying DeepInterpolation to in vivo two-photon Ca2+ imaging yields up to 6 times more segmented neuronal segments with a 15 fold increase in single pixel SNR, uncovering network dynamics at the single-trial level. In extracellular electrophysiology recordings, DeepInterpolation recovered 25% more high-quality spiking units compared to a standard data analysis pipeline. On fMRI datasets, DeepInterpolation increased the SNR of individual voxels 1.6-fold. All these improvements were attained without sacrificing spatial or temporal resolution. DeepInterpolation could well have a similar impact in other domains for which independent noise is present in experimental data.

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Lecoq, J., Oliver, M., Siegle, J. H., Orlova, N., Koch, C.. 2020-10-16. Removing independent noise in systems neuroscience data using DeepInterpolation. https://doi.org/10.1101/2020.10.15.341602

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