Spatiotemporal clustering of synchronized bursting events in neuronal networks

Abstract

In vitro neuronal networks display synchronized bursting events (SBEs), with characteristic temporal width of 100–500 ms and frequency of once every few seconds. We analyze such data using preprocessing by SVD for dimensional reduction, after which we apply quantum clustering to sort out the SBEs into different groups. Each SBE is described within a spatiotemporal template, allowing us to distinguish between neuronal activities (firing rates) and relative temporal variations of the spiking of each neuron within the SBE. Clustering assignments according to the two different kinds of information are very different from one another. SBEs may thus serve as carriers of different information in these two different implementations of the neuronal code.