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Free keywords:
Action Potentials/*physiology
Algorithms
Animals
*Cluster Analysis
Computer Simulation
Female
Grasshoppers
Male
*Models, Neurological
Models, Statistical
Neurons/*classification/*physiology
Pattern Recognition, Automated
Quality Control
Sensitivity and Specificity
Sensory Receptor Cells/physiology
*Signal Processing, Computer-Assisted
Stochastic Processes
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.
