Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising

Abstract

Calcium imaging has transformed neuroscience research by providing a methodology for monitoring the activity of neural circuits with single-cell resolution. However, calcium imaging is inherently susceptible to detection noise, especially when imaging with high frame rate or under low excitation dosage. Here we developed DeepCAD, a self-supervised deep-learning method for spatiotemporal enhancement of calcium imaging data that does not require any high signal-to-noise ratio (SNR) observations. DeepCAD suppresses detection noise and improves the SNR more than tenfold, which reinforces the accuracy of neuron extraction and spike inference and facilitates the functional analysis of neural circuits. DeepCAD is a self-supervised deep-learning approach for denoising calcium imaging data. DeepCAD improved SNR and facilitates neuron extraction and spike inference.