Abstract
Accurate identification of bursting activity is an essential element in the characterization of neuronal network activity. Despite this, no one technique for identifying bursts in spike trains has been widely adopted. Instead, many methods have been developed for the analysis of bursting activity, often on an ad hoc basis. Here we provide an unbiased assessment of the effectiveness of eight of these methods at detecting bursts in a range of spike trains. We suggest a list of features that an ideal burst detection technique should possess and use synthetic data to assess each method in regard to these properties. We further employ each of the methods to reanalyze microelectrode array (MEA) recordings from mouse retinal ganglion cells and examine their coherence with bursts detected by a human observer. We show that several common burst detection techniques perform poorly at analyzing spike trains with a variety of properties. We identify four promising burst detection techniques, which are then applied to MEA recordings of networks of human induced pluripotent stem cell-derived neurons and used to describe the ontogeny of bursting activity in these networks over several months of development. We conclude that no current method can provide "perfect" burst detection results across a range of spike trains; however, two burst detection techniques, the MaxInterval and logISI methods, outperform compared with others. We provide recommendations for the robust analysis of bursting activity in experimental recordings using current techniques.
Highlights
We provide an unbiased quantitative assessment of eight existing methods for identifying bursts in neuronal spike trains
The MI, cumulative moving average (CMA), and logISI methods followed the trend of decreasing burst duration with age, as in the original analysis, while the Poisson surprise (PS) method detected a significant increase in burst duration at P15
The results suggest that the prevalence of bursting activity in these networks may decrease with age after reaching a peak around 14 weeks after plating (WAP) (Fig. 7B)
Summary
We provide an unbiased quantitative assessment of eight existing methods for identifying bursts in neuronal spike trains. One common approach to burst detection is identification of periods of bursting using simple thresholds, which impose limits on values such as the minimum firing rate or maximum allowed interspike interval (ISI) in a burst These thresholds can either be fixed values (Chiappalone et al 2005; Mukai et al 2003) or derived from properties of the spike trains, such as the mean ISI (Chen et al 2009), total spiking rate (Pimashkin et al 2011), or some form of the distribution of ISIs or discharge density (Bakkum et al 2013; Cocatre-Zilgien and Delcomyn 1992; Kaneoke and Vitek 1996; Kapucu et al 2012; Pasquale et al 2010; Selinger et al 2007). It has been demonstrated that networks of www.jn.org
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