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Journal of Computational Neuroscience

, Volume 29, Issue 1–2, pp 213–229 | Cite as

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

  • Valentina Pasquale
  • Sergio Martinoia
  • Michela Chiappalone
Article

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.

Keywords

Logarithmic ISI histogram Non-parametric burst detection Network bursts Neuronal cultures Micro-electrode arrays 

Abbreviations

MEA

Micro-electrode array

BD

Burst detection

NBD

Network burst detection

DIV

Days in vitro

BIC

Bicuculline

APV

D-2-amino-5-phosphonopentanoic acid

ISI

Inter-spike interval

logISIH

Logarithmic inter-spike interval histogram

ISIth

Inter-spike interval threshold

IBeI

Inter-burst event interval

logIBeIH

Logarithmic inter-burst event interval histogram

IBeIth

Inter-burst event interval threshold

newBD

New burst detection algorithm

SE

Selinger’s algorithm

CH

Chiappalone’s algorithm

WA

Wagenaar’s algorithm

GE

Gourevitch-Eggermont’s algorithm

SEM

Standard error of the mean

Notes

Acknowledgments

The authors wish to thank Dr. Mariateresa Tedesco for excellent culture preparation and maintenance and for providing images for Fig. 1(a). The authors are very grateful to Dr Luca Leonardo Bologna, who provided the recordings of spontaneous activity during development.

Supplementary material

10827_2009_175_MOESM1_ESM.doc (46 kb)
S1 A self-adapting approach for the detection of bursts and network bursts in neuronal cultures (DOC 46 kb)

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Valentina Pasquale
    • 1
  • Sergio Martinoia
    • 2
    • 1
  • Michela Chiappalone
    • 1
  1. 1.Neuroscience and Brain Technologies DepartmentItalian Institute of TechnologyGenovaItaly
  2. 2.Neuroengineering and Bio-nanoTechnology Laboratory, Department of Biophysical and Electronic EngineeringUniversity of GenovaGenovaItaly

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