A self-adapting approach for the detection of bursts and network bursts in neuronal cultures.

Dissociated networks of neurons typically exhibit bursting behavior, whose features are strongly influenced by the age of the culture, by chemical/electrical stimulation or by environmental conditions. To help the experimenter in identifying the changes possibly induced by specific protocols, we developed a self-adapting method for detecting both bursts and network bursts from electrophysiological activity recorded by means of micro-electrode arrays. The algorithm is based on the computation of the logarithmic inter-spike interval histogram and automatically detects the best threshold to distinguish between inter- and intra-burst inter-spike intervals for each recording channel of the array. An analogous procedure is followed for the detection of network bursts, looking for sequences of closely spaced single-channel bursts. We tested our algorithm on recordings of spontaneous as well as chemically stimulated activity, comparing its performance to other methods available in the literature.