Thomas Kreuz and Nebojsa Bozanic
Matlab Graphical User Interface (GUI) for monitoring spike train synchrony
Kreuz T, Mulansky M, Bozanic N:
SPIKY: A graphical user interface for monitoring spike train synchrony
JNeurophysiol 113, 3432 (2015) [PDF]
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 that 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 the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that 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.
Mulansky M, Kreuz T:
PySpike - A Python library for analyzing spike train synchrony
Software X 5, 183 (2016) [PDF]
Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute similarity and dissimilarity profiles, averaged values and distance matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.
Matlab Command Line Library for monitoring spike train synchrony
There is no publication on cSPIKE but for more general information please refer to page 11 of the doctoral thesis of Eero Satuvori (which is also reproduced below):
1.4 cSPIKE software
During my studies, I wrote a freely available spike train analysis program called cSPIKE. This software is used for doing all the spike train analysis concerning ISI-distance, SPIKE-distance and SPIKE-synchronization and their variants. All in all it includes over 40 functions for calculating different kinds of distances between spike trains. The distance measures have been implemented before in a Matlab GUI package SPIKY as well as in a Python package PySpike. Unlike the previous user friendly graphical user interface SPIKY, cSPIKE uses command line interface and is intended to be used by those, who are more familiar with Matlab coding. cSPIKE differs from the PySpike mainly by the implementation platform. Both use C++ for speed and run from command line interface. Additionally, the reduction of output data required for running cSPIKE when compared to SPIKY GUI allows the computation to be performed considerably faster.