RT Journal Article SR Electronic T1 Effective and Efficient Neural Networks for Spike Inference from In Vivo Calcium Imaging JF bioRxiv FD Cold Spring Harbor Laboratory SP 2021.08.30.458217 DO 10.1101/2021.08.30.458217 A1 Zhanhong Zhou A1 Hei Matthew Yip A1 Katya Tsimring A1 Mriganka Sur A1 Jacque Pak Kan Ip A1 Chung Tin YR 2022 UL http://biorxiv.org/content/early/2022/04/04/2021.08.30.458217.abstract AB 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.Competing Interest StatementThe authors have declared no competing interest.