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Updated: Jul 10, 2025

Fluorescent Calcium Imaging and Subsequent In Situ Hybridization for Neuronal Precursor Characterization in Xenopus laevis
Published on: February 18, 2020
An integrate-and-fire approach to Ca2+ signaling. Part II: Cumulative refractoriness
Lukas Ramlow1, Martin Falcke2, Benjamin Lindner3
1Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany; Department of Physics, Humboldt University Berlin, Berlin, Germany; Max Delbrück Center for Molecular Medicine, Berlin, Germany.
Abstract:
Inositol 1,4,5-trisphosphate-induced Ca2+ signaling is a second messenger system used by almost all eukaryotic cells. The agonist concentration stimulating Ca2+ signals is encoded in the frequency of a Ca2+ concentration spike sequence. When a cell is stimulated, the interspike intervals (ISIs) often show a distinct transient during which they gradually increase, a system property we refer to as cumulative refractoriness. We extend a previously published stochastic model to include the Ca2+ concentration in the intracellular Ca2+ store as a slow adaptation variable. This model can reproduce both stationary and transient statistics of experimentally observed ISI sequences. We derive approximate expressions for the mean and coefficient of variation of the stationary ISIs. We also consider the response to the onset of a constant stimulus and estimate the length of the transient and the strength of the adaptation of the ISI. We show that the adaptation sets the coefficient of variation in agreement with current ideas derived from experiments. Moreover, we explain why, despite a pronounced transient behavior, ISI correlations can be weak, as often observed in experiments. Finally, we fit our model to reproduce the transient statistics of experimentally observed ISI sequences in stimulated HEK cells. The fitted model is able to qualitatively reproduce the relationship between the stationary interval correlations and the number of transient intervals, as well as the strength of the ISI adaptation. We also find positive correlations in the experimental sequence that cannot be explained by our model.
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