Latency correction in sparse neuronal spike trains

TL;DR

This paper introduces a multivariate latency correction algorithm for sparse neuronal spike trains, improving the accuracy of spike timing analysis in neurophysiological data affected by response delays or propagation speed variations.

Contribution

The authors develop a novel algorithm that corrects systematic latency shifts in sparse spike trains using spike matching and simulated annealing, suitable for data where timing is more informative than firing rate.

Findings

Effective on simulated and real calcium imaging data from mice

Criterion established to predict algorithm's applicability to new datasets

Source code made publicly available for easy implementation

Abstract

Background: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. New Method: We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing. Results: We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset. Comparison with Existing Method(s): Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information. Conclusions: For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.