Title
Formulation and validation of a method for classifying neurons from multielectrode recordings
Abstract
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.
Year
DOI
Venue
2005
10.1007/11499220_7
IWINAC (1)
Keywords
Field
DocType
latency response,simple method,multielectrode recording,central topic,initial set,coherent subpopulations,different class,classification problem,classifying neuron,linear discriminant analysis,different behaviour,artificial neural network
Histogram,Spike sorting,Pattern recognition,Computer science,Multivariate statistics,Euclidean distance,Coding (social sciences),Artificial intelligence,Linear discriminant analysis,Artificial neural network,Multielectrode array,Machine learning
Conference
Volume
ISSN
ISBN
3561
0302-9743
3-540-26298-9
Citations 
PageRank 
References 
0
0.34
2
Authors
4
Name
Order
Citations
PageRank
M. P. Bonomini112.17
J. M. Ferrández284.63
J. A. Bolea300.34
Eduardo B. Fernandez41653429.84