Effective and Efficient Neural Networks for Spike Inference from In Vivo Calcium Imaging

Abstract

Calcium imaging technique provides the advantages in monitoring large population of neuronal activities simultaneously. However, it lacks the signal quality provided by neural spike recording in traditional electrophysiology. To address this issue, we developed a supervised data-driven approach to extract spike information from calcium signals. We propose the ENS² (effective and efficient neural networks for spike inference from calcium signals) system for spike-rate and spike-event predictions using raw calcium inputs based on U-Net deep neural network. When testing on a large, ground truth public database, it consistently outperformed state-of-the-arts algorithms in both spike-rate and spike-event predictions with reduced computational load. We further demonstrated that ENS² would improve analyses of orientation selectivity in primary visual cortex neurons. We concluded that optimizing our system for spike-event prediction would produce a versatile inference system that benefits diverse neuroscience studies.