title: The M-Sorter: an automatic and robust spike detection and classification system.
abstract: Neural spike detection and classification, or spike sorting, is the first and a critical step prior to any single unit based neuroscientific studies and applications. A good spike sorter is usually characterized by high detection and classification accuracy, robust to changes in signal-to-noise ratio, objectivity in detection results or less user dependency, and real-time applicability. Here we present an automatic and robust spike detection and classification system, the M-Sorter, based on the multiple correlation of wavelet coefficients (MCWC) detection algorithm in conjunction with template matching for classification. Unlike many existing spike sorters that make use of a series of complex spike classifiers to deal with the challenges resulted from a low performance spike detector, the M-Sorter relies on a high performance yet computationally efficient detection algorithm and thus a simple classifier suffices to generate high quality spike sorting results. In this paper we provide step by step implementation procedures of the M-Sorter, and compare its performance with other popular sorters.
authors: Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USA. yuan.yuan.1@asu.edu
authors: Yuan
authors: Y
authors: Yuan
authors: Chenhui
authors: C
authors: Yang
authors: Jennie
authors: J
authors: Si
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D008959:Models, Neurological; D009474:Neurons / Q000145:classification* / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio