Two-photon imaging is a major recording technique in neuroscience but its low sampling rate imposes a severe limit of elucidating high temporal profiles of neuronal dynamics. Here we developed two hyperacuity Bayesian algorithms to improve spike detection and spike time precision, minimizing the estimation error supervised by the ground-truth given as the electrical spike signals. The benchmark showed that our algorithms outperformed other unsupervised algorithms maximizing the likelihood of the estimates for both experimental and simulation data. We argue that the supervised algorithms are useful tools to improve spike estimation of two-photon recording in case ground truth signals are available.
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