Comparison between supervised and unsupervised classifications of neuronal cell types: a case study.

ABSTRACT: In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a data

SUBMITTER: Guerra L 

PROVIDER: S-EPMC3058840 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature