A novel algorithm for burst and network burst detection Application to wild-type and SynI knockout mice cultures for the study of epileptogenesis

In vitro networks of neurons typically exhibit bursting behavior. To help the experimenter in characterizing different preparations or identifying the changes possibly induced by specific protocols, we developed a new automated method for detecting both bursts and network bursts from electrophysiological activity recorded by means of micro-electrode arrays. The burst detection algorithm is based on the computation of the logarithmic inter-spike interval histogram to automatically detect the best threshold to distinguish between inter- and intra-burst spikes. We preliminary tested our new tools on spontaneous and chemically stimulated activity recordings from primary cultures of rat cortex neurons. Moreover, as a case study, we also applied our algorithms to recordings coming from embryonic primary cortical neurons of wild-type and SynI knockout mice. Our results show i) the flexibility and self-adapting properties of the proposed methods, by applying them to different kind of preparations; ii) their capability of revealing changes in the bursting activity due to different experimental conditions.