Burst detection in neuronal activity

Neurons in the brain fire action potentials or spikes to encode information and communicate with each other. The firing can be in tonic mode, where individual spikes are emitted at relatively distant time intervals, and in burst mode, where neurons typically fire trains of spikes in short temporal succession. Functionally, neuronal bursts may be involved in reliable information transmission or specialized information encoding schemes. Understanding the dynamics of neuronal burst activity plays a crucial role in unravelling the complex mechanisms underlying neural information processing. However, detecting bursts in extracellularly recorded neural data is far from trivial. There are many methods for burst detection, but none was adopted universally, and they are based exclusively on the evaluation of spike times. This article aims to compare these burst detection algorithms against each other from several perspectives in order to establish the most suitable and robust one. Moreover, we propose a novel burst detection method considering additional features that contain as much information as possible from the recording.