Spike Train Synchrony Analysis of Neuronal Cultures

Spike train synchrony estimation of neuronal cultures provides valuable insights into firing patterns of neurons in terms of degree of similarity or dissimilarity. These estimations have proven to be a useful tool in neuroscience since synchrony in neuronal networks is thought to be related to cognitive processes, sensory awareness, learning and neurological disorders. Many mathematical measures have been developed to quantify the degree of synchrony. These synchrony metrics are generally used for smaller sets of spike trains and not been explored for larger High Density Multi Electrode Arrays (HD-MEA) datasets with thousands of channels. Here, bivariate and multivariate ISI-distance and SPIKE-distance metrics are utilized on both synthetic and experimental HD-MEA datasets to quantify spike train synchrony. It is demonstrated that, despite the significant size of the datasets, the approaches are effective in identifying and quantifying interesting bursting or change in spike train behaviours which are not always obvious from the raster plot.