Spike detection for calcium activity

Hermine Biermé (1) , Camille Constant (1) , Anne Duittoz (2, 3) , Christine Georgelin (4)

1 LMA [Poitiers] - Laboratoire de mathématiques et applications [UMR 7348]
2 UT - Université de Tours
3 PRC - Physiologie de la reproduction et des comportements [Nouzilly]
4 IDP - Institut Denis Poisson

We present in this paper a global methodology for the spike detection in a biological context of fluorescence recording of GnRh-neurons calcium activity. For this purpose we first propose a simple stochastic model that could mimic experimental time series by considering an autoregressive AR(1) process with a linear trend and specific innovations involving spiking times. Estimators of parameters with asymptotic normality are established and used to set up a statistical test on estimated innovations in order to detect spikes. We compare several procedures and illustrate on biological data the performance of our procedure.

Mots clés: Autoregressive process; time series; peaks detection; neuronal activity

Domaines: Statistiques [stat]; Probabilités [math.PR]

Hermine Biermé, Camille Constant, Anne Duittoz, Christine Georgelin. Spike detection for calcium activity. The international journal of biostatistics, 2022, ⟨10.1515/ijb-2020-0043⟩. ⟨hal-02370401v3⟩