16/11 – Jérôme Lecoq : Removing independent noise in systems neuroscience data using DeepInterpolation and some updates on the OpenScope platform
SPPIN’s webinar 2021, 16 NOVEMBER at 16h, online – Ask link to a SPPIN member.
Jérôme Lecoq, Ph.D. Principal Scientist, Allen Institute for Brain Science, United States
Removing independent noise in systems neuroscience data using DeepInterpolation and some updates on the OpenScope platform
In this talk, we will first 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 six times more neuronal segments than those computed from raw data with a 15-fold increase in the single-pixel signal-to-noise ratio (SNR), uncovering single-trial network dynamics that were previously obscured by noise. Extracellular electrophysiology recordings processed with DeepInterpolation yielded 25% more high-quality spiking units than those computed from raw data, while DeepInterpolation produced a 1.6-fold increase in the SNR of individual voxels in fMRI datasets. 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.
We will finish the talk with some updates on the OpenScope platform, the first community-driven brain observatory now funded to serve neuroscientists worldwide (https://www.youtube.com/watch?v=_h2UcEWZ4Jk).