A calcium imaging method has superior ability in recording of spatial-temporal variations in ion concentration. However, it has two major problems. First, the imaging signals are very noisy. Second, the observation data are only the fluorescence intensities of Ca2+ indicator dyes that provide indirect information about the Ca2+ concentration. We develop a nonlinear state-space model for Ca imaging series involving Ca 2+ kinetics and a noisy fluorescence intensity pickup process. We devise recursive update algorithms for estimating the Ca2+ concentration and Ca2+ flux, and give the expectationmaximization algorithm for inferring model parameters.
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