No abstract

Calcium fluorescence imaging enables real-time activity monitoring of single neurons in living animals. A critical inverse problem resides in the precise inference of spike locations from noisy fluorescence traces, especially in the presence of burst spiking and non-linear fluorescence intensity. Several spike extraction algorithms have been proposed in the recent years, but a robust and objective evaluation of their performance still remains elusive due to the unsupervised nature of the problem. Here we propose a biologically-inspired mathematical framework to reproduce synthetic fluorescence traces from a time-series data of spike-trains. The idea is to create a versatile platform to objectively test the state-ofthe art spike inference methodologies over a large range of experimental parameters. Our solution appears as a complementary but more exhaustive approach to determine the robustness of existing solutions to different nature of signals, imaging artefacts, sensitivity to hyper-parameters and pre-processing steps. We benchmark state-of-the-art algorithms with the proposed simulation platform, and validate the results on an experimental dataset of the Hydra Vulgaris. We hypothesize that, in contrast to the common practice of qualitative evaluation, quantitative measure of algorithm robustness is essential in understanding the suitability of a spike inference algorithm to be used in an automated computational pipeline to decipher the neural code.

The observation of physical phenomena often goes through the recording of discrete time series of events, that can be represented with marked point processes. The robust estimation of the correlation between two point processes can, therefore, unveil physical mechanisms underlying the observed phenomena. However, the robust estimation of correlation between two, or more, point-processes is hindered by the signal noise (leading to false and missing point detections), the important density of points, and possible time-shift between coupled points. We propose a statistical framework that uses hypothesis testing to estimate coupling between time pointprocesses. Using simulations, we show that our framework accurately estimates the coupling between two time point-processes even for noisy signal (with false point detections), for high density of points and in the presence of a time shift between coupled points. By applying our statistical framework to the recordings of neuron population activity in mouse visual cortex, we measure the functional coupling between individual neurons, and cluster neurons into functional ensembles.