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Parameters for burst detection
Bursts of action potentials within neurons and throughout networks are believed to serve roles in how neurons handle and store information, both in vivo and in vitro. Accurate detection of burst occurrences and durations are therefore crucial for many studies. A number of algorithms have been propos...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3915237/ https://www.ncbi.nlm.nih.gov/pubmed/24567714 http://dx.doi.org/10.3389/fncom.2013.00193 |
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author | Bakkum, Douglas J. Radivojevic, Milos Frey, Urs Franke, Felix Hierlemann, Andreas Takahashi, Hirokazu |
author_facet | Bakkum, Douglas J. Radivojevic, Milos Frey, Urs Franke, Felix Hierlemann, Andreas Takahashi, Hirokazu |
author_sort | Bakkum, Douglas J. |
collection | PubMed |
description | Bursts of action potentials within neurons and throughout networks are believed to serve roles in how neurons handle and store information, both in vivo and in vitro. Accurate detection of burst occurrences and durations are therefore crucial for many studies. A number of algorithms have been proposed to do so, but a standard method has not been adopted. This is due, in part, to many algorithms requiring the adjustment of multiple ad-hoc parameters and further post-hoc criteria in order to produce satisfactory results. Here, we broadly catalog existing approaches and present a new approach requiring the selection of only a single parameter: the number of spikes N comprising the smallest burst to consider. A burst was identified if N spikes occurred in less than T ms, where the threshold T was automatically determined from observing a probability distribution of inter-spike-intervals. Performance was compared vs. different classes of detectors on data gathered from in vitro neuronal networks grown over microelectrode arrays. Our approach offered a number of useful features including: a simple implementation, no need for ad-hoc or post-hoc criteria, and precise assignment of burst boundary time points. Unlike existing approaches, detection was not biased toward larger bursts, allowing identification and analysis of a greater range of neuronal and network dynamics. |
format | Online Article Text |
id | pubmed-3915237 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-39152372014-02-24 Parameters for burst detection Bakkum, Douglas J. Radivojevic, Milos Frey, Urs Franke, Felix Hierlemann, Andreas Takahashi, Hirokazu Front Comput Neurosci Neuroscience Bursts of action potentials within neurons and throughout networks are believed to serve roles in how neurons handle and store information, both in vivo and in vitro. Accurate detection of burst occurrences and durations are therefore crucial for many studies. A number of algorithms have been proposed to do so, but a standard method has not been adopted. This is due, in part, to many algorithms requiring the adjustment of multiple ad-hoc parameters and further post-hoc criteria in order to produce satisfactory results. Here, we broadly catalog existing approaches and present a new approach requiring the selection of only a single parameter: the number of spikes N comprising the smallest burst to consider. A burst was identified if N spikes occurred in less than T ms, where the threshold T was automatically determined from observing a probability distribution of inter-spike-intervals. Performance was compared vs. different classes of detectors on data gathered from in vitro neuronal networks grown over microelectrode arrays. Our approach offered a number of useful features including: a simple implementation, no need for ad-hoc or post-hoc criteria, and precise assignment of burst boundary time points. Unlike existing approaches, detection was not biased toward larger bursts, allowing identification and analysis of a greater range of neuronal and network dynamics. Frontiers Media S.A. 2014-01-13 /pmc/articles/PMC3915237/ /pubmed/24567714 http://dx.doi.org/10.3389/fncom.2013.00193 Text en Copyright © 2014 Bakkum, Radivojevic, Frey, Franke, Hierlemann and Takahashi. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Bakkum, Douglas J. Radivojevic, Milos Frey, Urs Franke, Felix Hierlemann, Andreas Takahashi, Hirokazu Parameters for burst detection |
title | Parameters for burst detection |
title_full | Parameters for burst detection |
title_fullStr | Parameters for burst detection |
title_full_unstemmed | Parameters for burst detection |
title_short | Parameters for burst detection |
title_sort | parameters for burst detection |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3915237/ https://www.ncbi.nlm.nih.gov/pubmed/24567714 http://dx.doi.org/10.3389/fncom.2013.00193 |
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