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Burst Detection Methods.

Ellese Cotterill1, Stephen J Eglen2

  • 1Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.

Advances in Neurobiology
|May 11, 2019
PubMed
Summary
This summary is machine-generated.

This chapter reviews computational methods for detecting neuronal bursting, periods of high-frequency neuron firing. Accurate detection is crucial for understanding neural network activity and development.

Keywords:
Burst detectionMultielectrode arraysSpike train analysis

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Electrophysiology

Background:

  • Neuronal bursting, characterized by high-frequency firing followed by quiescence, is observed in various neural systems.
  • Bursting is linked to information transfer, network formation during development, and is sensitive to genetic/pharmacological factors.
  • Accurate detection of bursting is vital for characterizing neuronal network activity.

Purpose of the Study:

  • To review computational methods for detecting neuronal bursting activity in spike train data.
  • To highlight popular and successful algorithms used in neuroscience research.

Main Methods:

  • Review of computational techniques for spike train analysis.
  • Focus on algorithms designed to identify bursting patterns.

Main Results:

  • Multiple computational methods exist for detecting neuronal bursting.
  • The chapter provides an overview of established and effective detection techniques.

Conclusions:

  • Computational methods are essential tools for analyzing neuronal bursting.
  • Understanding these methods aids in characterizing neural network function and development.