Name
Title | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s10827-009-0175-1 | Journal of Computational Neuroscience |
Keywords | Field | DocType |
Logarithmic ISI histogram,Non-parametric burst detection,Network bursts,Neuronal cultures,Micro-electrode arrays | Bursting,Histogram,Computer science,Communication channel,Artificial intelligence,Logarithm,Electrophysiology,Machine learning,Computation | Journal |
Volume | Issue | ISSN |
29 | 1-2 | 1573-6873 |
Citations | PageRank | References |
11 | 1.02 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valentina Pasquale | 1 | 23 | 3.66 |
| S Martinoia | 2 | 158 | 20.68 |
| Michela Chiappalone | 3 | 61 | 10.06 |