A review of methods for spike sorting: the detection and classification of neural action potentials.
The detection of neural spike activity is a technical challenge that is a prerequisite for studying many types of brain function. Measuring the activity of individual neurons accurately can be difficult due to large amounts of background noise and the difficulty in distinguishing the action potentials of one neuron from those of others in the local area. This article reviews algorithms and methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting. The article first discusses the challenges of measuring neural activity and the basic issues of signal detection and classification. It reviews and illustrates algorithms and techniques that have been applied to many of the problems in spike sorting and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands. The article is written both for the physiologist wanting to use simple methods that will improve experimental yield and minimize the selection biases of traditional techniques and for those who want to apply or extend more sophisticated algorithms to meet new experimental challenges.
Lewicki
M S
MS
Howard Hughes Medical Institute, Computational Neurobiology Laboratory, The Salk Institute, La Jolla, CA 92037, USA. lewicki@salk.edu
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D002965:Classification; D016000:Cluster Analysis; D004594:Electrophysiology; D009474:Neurons / Q000502:physiology*