Formulation and Validation of a Method for Classifying Neurons from Multielectrode Recordings
The issue of classification has long been a central topic in the analysis of multielectrode data, either for spike sorting or for getting insight into interactions among ensembles of neurons. Related to coding, many multivariate statistical techniques such as linear discriminant analysis (LDA) or artificial neural networks (ANN) have been used for dealing with the classification problem providing very similar performances. This is, there is no method that stands out from others and the right decision about which one to use is mainly depending on the particular cases demands. Therefore, we developed and validated a simple method for classification based on two different behaviours: periodicity and latency response. The method consists of creating sets of relatives by defining an initial set of templates based on the autocorrelograms or peristimulus time histograms (PSTHs) of the units and grouping them according to a minimal Euclidian distance among the units in a class and maximizing it among different classes. It is shown here the efficiency of the method for identifying coherent subpopulations within multineuron populations.
KeywordsLinear Discriminant Analysis Retinal Ganglion Cell Multivariate Statistical Technique Multielectrode Array Minimal Euclidian Distance
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