SPIKY: A graphical user interface for monitoring spike train synchrony

TL;DR

SPIKY is a user-friendly graphical interface that simplifies the application of advanced spike train synchrony measures, enabling efficient analysis of large-scale neuronal data for researchers.

Contribution

It introduces a graphical user interface for spike train synchrony measures, making complex analysis accessible and efficient for users without extensive Matlab knowledge.

Findings

Includes optimized implementations of ISI-distance, SPIKE-distance, and SPIKE-synchronization.

Provides tools for analyzing both simulated and real neuronal data.

Facilitates large dataset analysis and significance estimation.

Abstract

Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface which facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance and SPIKE-synchronization (an improved and simplified extension of event synchronization) which have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector which makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels.