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

Abstract

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.

Modulation of network activity and induction of homeostatic synaptic plasticity by enzymatic removal of heparan sulfates

“…The detection of bursts was based on the detection of peaks in the inter-spike interval histogram. To detect bursts of bursts in the spiking activity, we applied the threshold detection algorithm, in which the threshold was estimated using the inter-burst interval histogram as the value corresponding to the minimum between the peak for regular bursts and the next peak [30].…”

In vitro homogeneous and heterogeneous interconnected neuronal cultures: Exploring expressed dynamics and functional connectivity

“…Spontaneous spiking activity was detected using threshold based 'Precise Timing Spike Detection' (PTSD) algorithm [8], while bursting activity (i.e., fast sequence of spikes) was detected using the algorithm devised by Pasquale et al [9]. The latter algorithm is based on computation of the logarithmic inter-spike interval histogram to detect automatically the best threshold between inter-burst (i.e., between bursts and/or outside bursts) and intra-burst (i.e., within burst) activity for each recording channel of the array.…”

A new cross-correlation algorithm for the analysis of “in vitro” neuronal network activity aimed at pharmacological studies

“…A threshold method is applied to identify the initial and the final points of the NB; bins belong to a NB if the computed product in that NB is bigger than 9 (i.e. the threshold value suggested in the literature) and if other bins over threshold are present within 2 s (maximum intra NB interval is 2 s (Pasquale et al, 2010)). Finally, only NBs that involve more than 80% of total active electrodes are chosen, as previously done by Segev et al (2004) and the remaining recorded data are discarded.…”