Using noise signature to optimize spike-sorting and to assess neuronal classification quality

J Neurosci Methods. 2002 Dec 31;122(1):43-57. doi: 10.1016/s0165-0270(02)00276-5.


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.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.
  • Validation Study

MeSH terms

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