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Detection of bursts and pauses in spike trains


, : Detection of bursts and pauses in spike trains. Journal of Neuroscience Methods 211(1): 145-158

Midbrain dopaminergic neurons in vivo exhibit a wide range of firing patterns. They normally fire constantly at a low rate, and speed up, firing a phasic burst when reward exceeds prediction, or pause when an expected reward does not occur. Therefore, the detection of bursts and pauses from spike train data is a critical problem when studying the role of phasic dopamine (DA) in reward related learning, and other DA dependent behaviors. However, few statistical methods have been developed that can identify bursts and pauses simultaneously. We propose a new statistical method, the Robust Gaussian Surprise (RGS) method, which performs an exhaustive search of bursts and pauses in spike trains simultaneously. We found that the RGS method is adaptable to various patterns of spike trains recorded in vivo, and is not influenced by baseline firing rate, making it applicable to all in vivo spike trains where baseline firing rates vary over time. We compare the performance of the RGS method to other methods of detecting bursts, such as the Poisson Surprise (PS), Rank Surprise (RS), and Template methods. Analysis of data using the RGS method reveals potential mechanisms underlying how bursts and pauses are controlled in DA neurons. This paper proposes a new method for analyzing spike trains that can detect bursts and pauses simultaneously Bursts and pauses are detected independent of background firing rate Normalized spike trains can be pooled within groups to obtain a central distribution even when there are only bursts and pauses Simultaneous detection of bursts and pauses provides insight into the underlying mechanism driving bursts in dopaminergic neurons.

US$19.90

PMID: 22939922

DOI: 10.1016/j.jneumeth.2012.08.013


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