High frequency spike inference with particle Gibbs sampling

Abstract

Fluorescent calcium indicators are indispensable tools for monitoring the spiking activity of large neuronal populations in animal models. However, despite the plethora of algorithms developed over the last decades, accurate spike time inference methods for spike rates greater than 20 Hz are lacking. More importantly, little attention has been devoted to the quantification of statistical uncertainties in spike time estimation, which is essential for assigning confidence levels to inferred spike patterns. To address these challenges, we introduce (1) a statistical model that accounts for bursting neuronal activity and baseline fluorescence modulation and (2) apply a Monte Carlo strategy (particle Gibbs with ancestor sampling) to estimate the joint posterior distribution of spike times and model parameters. Our method is competitive with state-of-the-art supervised and unsupervised algorithms by analyzing the CASCADE benchmark datasets. The analysis of fluorescence transients recorded using an ultrafast genetically encoded calcium indicator, GCaMP8f, demonstrates the ability of our method to infer spike time intervals as short as five milliseconds. Overall, our study describes a Bayesian inference method to detect neuronal spiking patterns and their uncertainty. The use of particle Gibbs samplers allows for unbiased estimates of spike times and all model parameters, and it provides a flexible statistical framework to test more specific models of calcium indicators.