ArticleNeuroinformatics2019
Morphological Neuron Classification Based on Dendritic Tree Hierarchy.
Article in Neuroinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- SPCNNet: spiking point cloud neural network for morphological neuron classification.Scientific reports · 2026Article
- Identifying and investigating emergent behaviour in neural systems.Frontiers in neuroscience · 2026Article
- Inhibition of neuronal necroptosis via disruption of RIPK1-RIPK3 Interactions: The role of neural stem cell-derived exosomes in spinal cord injury recovery.Bioactive materials · 2025Article
- Morphological classification of neurons based on Sugeno fuzzy integration and multi-classifier fusion.Scientific reports · 2024Article
- Multi-level feature fusion network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Generation of an enhancer-driven gene expression viral tool specific to dentate granule cell-types through direct hippocampal injection.Frontiers in neuroscience · 2024Article
- NeuroPath2Path: Classification and elastic morphing between neuronal arbors using path-wise similarity.Neuroinformatics · 2020Article
- A Method for the Symbolic Representation of Neurons.Frontiers in neuroanatomy · 2018Article
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Authors and funding
4 authors.
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Abstract
The shape of a neuron can reveal interesting properties about its function. Therefore, morphological neuron characterization can contribute to a better understanding of how the brain works. However, one of the great challenges of neuroanatomy is the definition of morphological properties that can be used for categorizing neurons. This paper proposes a new methodology for neuron morphological analysis by considering different hierarchies of the dendritic tree for characterizing and categorizing neuronal cells. The methodology consists in using different strategies for decomposing the dendritic tree along its hierarchies, allowing the identification of relevant parts (possibly related to specific neuronal functions) for classification tasks. A set of more than 5000 neurons corresponding to 10 classes were examined with supervised classification algorithms based on this strategy. It was found that classification accuracies similar to those obtained by using whole neurons can be achieved by considering only parts of the neurons. Branches close to the soma were found to be particularly relevant for classification.
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