Multi-scale detection of rate changes in spike trains with weak dependencies

TL;DR

This paper extends a statistical test for detecting rate changes in neuronal spike trains to account for weak dependencies in inter spike intervals, improving accuracy in real-world data analysis.

Contribution

It introduces an extension of the Multiple Filter Test (MFT) that incorporates serial dependencies, enabling more accurate detection of change points in dependent spike train data.

Findings

Improved detection of change points in spike trains with serial correlations.

Reduced false positives in positively correlated spike trains.

Enhanced detection probability in negatively correlated spike trains.

Abstract

The statistical analysis of neuronal spike trains by models of point processes often relies on the assumption of constant process parameters. However, it is a well-known problem that the parameters of empirical spike trains can be highly variable, such as for example the firing rate. In order to test the null hypothesis of a constant rate and to estimate the change points, a Multiple Filter Test (MFT) and a corresponding algorithm (MFA) have been proposed that can be applied under the assumption of independent inter spike intervals (ISIs). As empirical spike trains often show weak dependencies in the correlation structure of ISIs, we extend the MFT here to point processes associated with short range dependencies. By specifically estimating serial dependencies in the test statistic, we show that the new MFT can be applied to a variety of empirical firing patterns, including positive and negative serial correlations as well as tonic and bursty firing. The new MFT is applied to a data set of empirical spike trains with serial correlations, and simulations show improved performance against methods that assume independence. In case of positive correlations, our new MFT is necessary to reduce the number of false positives, which can be highly enhanced when falsely assuming independence. For the frequent case of negative correlations, the new MFT shows an improved detection probability of change points and thus, also a higher potential of signal extraction from noisy spike trains.