Using noise signature to optimize spike-sorting and to assess neuronal classification quality.
J Neurosci Methods
; 122(1): 43-57, 2002 Dec 31.
Article
em En
| MEDLINE
| ID: mdl-12535763
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.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Sinais Assistido por Computador
/
Potenciais de Ação
/
Análise por Conglomerados
/
Modelos Neurológicos
/
Neurônios
Tipo de estudo:
Diagnostic_studies
/
Evaluation_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Revista:
J Neurosci Methods
Ano de publicação:
2002
Tipo de documento:
Article
País de afiliação:
Estados Unidos