title: Classification of neocortical interneurons using affinity propagation.
abstract: In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.
authors.affiliation: Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid Madrid, Spain ; Intelligent Systems Group, Department of Computer Science and Artificial Intelligence, University of The Basque Country San Sebastian, Spain.
authors.forename: Roberto
authors.initials: R
authors.lastname: Santana
authors.affiliation: Department Biological Sciences, Columbia University New York, NY, USA.
authors.forename: Laura M
authors.initials: LM
authors.lastname: McGarry
authors.affiliation: Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid Madrid, Spain.
authors.forename: Concha
authors.initials: C
authors.lastname: Bielza
authors.affiliation: Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid Madrid, Spain.
authors.forename: Pedro
authors.initials: P
authors.lastname: Larrañaga
authors.affiliation: Department Biological Sciences, Columbia University New York, NY, USA.
authors.forename: Rafael
authors.initials: R
authors.lastname: Yuste
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D007395:Interneurons / Q000145:classification* / Q000166:cytology / Q000502:physiology; D051379:Mice; D008822:Mice, Transgenic; D019579:Neocortex / Q000166:cytology* / Q000502:physiology
keywords: affinity propagation; cell types; cortex; interneurons