Image2SignalNet: Image-based deep learning approach for capturing neuronal signals from calcium imaging

Two-photon calcium imaging is a powerful technique for recording neuronal activities over extended periods. However, reliably capturing neuronal signals from the this data poses a significant challenge due to non-uniform neuropil distribution and densely packed neuronal populations. In this study, we leverage deep learning (DL) techniques to directly capture true neuronal signals from calcium imaging data, addressing these challenges effectively. Utilizing publicly available datasets, we demonstrate that our DL-based approach, which directly extracts neuronal signals from images, outperforms existing non-DL methods in accurately capturing neuronal signals. This highlights the significant potential of DL methods for unveiling neuronal activity hidden in calcium imaging data. Furthermore, we investigate the impact of calcium imaging data quality on the performance of DL models. While our approach demonstrates significant promise, ongoing improvements in the quality of calcium imaging data will further enhance DL techniques, leading to a deeper understanding of brain mechanisms.