Closed Form Jitter Analysis of Neuronal Spike Trains

TL;DR

This paper introduces a closed-form method for jitter analysis of neuronal spike trains, significantly improving computational efficiency and accuracy over traditional Monte Carlo approaches.

Contribution

It derives an exact, closed-form solution for spike train jitter analysis, reducing computational complexity and eliminating estimation errors in p-values.

Findings

Exact computation of spike train jitter distribution

Speed increase of two orders of magnitude over Monte Carlo methods

Elimination of approximation errors in p-value estimation

Abstract

Interval jitter and spike resampling methods are used to analyze the time scale on which temporal correlations occur. They allow the computation of jitter corrected cross correlograms and the performance of an associated statistically robust hypothesis test to decide whether observed correlations at a given time scale are significant. Currently used Monte Carlo methods approximate the probability distribution of coincidences. They require generating $N_{\rm MC}$ simulated spike trains of length $T$ and calculating their correlation with another spike train up to lag $\tau_{\max}$. This is computationally costly $O(N_{\rm MC} \times T \times \tau_{\max})$ and it introduces errors in estimating the $p$ value. Instead, we propose to compute the distribution in closed form, with a complexity of $O(C_{\max} \log(C_{\max}) \tau_{\max})$, where $C_{\max}$ is the maximum possible number of coincidences. All results are then exact rather than approximate, and as a consequence, the $p$-values obtained are the theoretically best possible for the available data and test statistic. In addition, simulations with realistic parameters predict a speed increase over Monte Carlo methods of two orders of magnitude for hypothesis testing, and four orders of magnitude for computing the full jitter-corrected cross correlogram.