title: Using noise signature to optimize spike-sorting and to assess neuronal classification quality.
abstract: We have developed a simple and expandable procedure for classification and validation of extracellular data based on a probabilistic model of data generation. This approach relies on an empirical characterization of the recording noise. We first use this noise characterization to optimize the clustering of recorded events into putative neurons. As a second step, we use the noise model again to assess the quality of each cluster by comparing the within-cluster variability to that of the noise. This second step can be performed independently of the clustering algorithm used, and it provides the user with quantitative as well as visual tests of the quality of the classification.
authors.affiliation: California Institute of Technology, Division of Biology, 139-74, Pasadena, CA 91125, USA. christophe.pouzat@biomedicale.univ-paris5.fr
authors.forename: Christophe
authors.initials: C
authors.lastname: Pouzat
authors.forename: Ofer
authors.initials: O
authors.lastname: Mazor
authors.forename: Gilles
authors.initials: G
authors.lastname: Laurent
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D003198:Computer Simulation; D005260:Female; D006110:Grasshoppers; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000145:classification* / Q000502:physiology*; D010363:Pattern Recognition, Automated; D011786:Quality Control; D012680:Sensitivity and Specificity; D011984:Sensory Receptor Cells / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes