Burst detection methods

TL;DR

This paper reviews various computational methods for detecting bursting activity in neuronal spike trains, which is crucial for understanding neuronal communication and network development.

Contribution

It provides a comprehensive overview of the most popular and successful burst detection algorithms used in neuronal data analysis.

Findings

Multiple burst detection methods are compared and evaluated.

The review highlights the strengths and limitations of each method.

It guides researchers in selecting appropriate tools for neuronal burst analysis.

Abstract

`Bursting', defined as periods of high frequency firing of a neuron separated by periods of quiescence, has been observed in various neuronal systems, both \textit{in vitro} and \textit{in vivo}. It has been associated with a range of neuronal processes, including efficient information transfer and the formation of functional networks during development, and has been shown to be sensitive to genetic and pharmacological manipulations. Accurate detection of periods of bursting activity is thus an important aspect of characterising both spontaneous and evoked neuronal network activity. A wide variety of computational methods have been developed to detect periods of bursting in spike trains recorded from neuronal networks. In this chapter, we review several of the most popular and successful of these methods.