Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification

Netanel Ofer, Orit Shefi, Gur Yaari

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and utilizing form-function principles in realistic neuronal reconstructions.

Original languageEnglish
Pages (from-to)581-590
Number of pages10
JournalNeuroinformatics
Volume18
Issue number4
DOIs
StatePublished - 1 Oct 2020

Bibliographical note

Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Interneuron classification
  • Neuromorphology
  • Neuronal coding

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