Towards a supervised classification of neocortical interneuron morphologies.

ABSTRACT:

Background

The challenge of classifying cortical interneurons is yet to be solved. Data-driven classification into established morphological types may provide insight and practical value.

Results

We trained models using 217 high-quality morphologies of rat somatosensory neocortex interneurons reconstructed by a single laboratory and pre-classified into eight types. We quantified 103 axonal and dendritic morphometrics, including novel ones that capture features such as arbor orientation, extent in layer one, and dendritic polarity. We trained a one-versus-rest classifier for each type, combining well-known supervised classification algorithms with feature selection and over- and under-sampling. We accurately classified the nest basket, Martinotti, and basket cell types with t

SUBMITTER: Mihaljevic B 

PROVIDER: S-EPMC6296106 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature