ArticleNeuroinformatics2020
Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification.
Article in Neuroinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Mitochondria delay action potential propagation.Communications biology · 2025Article
- Spike transmission failures in axons from cortical neuronsiScience · 2024Article
- Spike transmission failures in axons from mouse cortical pyramidal neurons in vivo.bioRxiv : the preprint server for biology · 2024Article
- Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.Molecules (Basel, Switzerland) · 2022Article
- A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination.Neuroinformatics · 2020Article
- Axonal Computations.Frontiers in cellular neuroscience · 2019Review
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Authors and funding
3 authors.
Funding
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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.
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Registered trials
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