ArticleFrontiers in neuroanatomy2016
Morphological Neuron Classification Using Machine Learning.
Article in Frontiers in neuroanatomy, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Application of Machine Learning Models to Identify Differences in Neural Electrophysiological Properties Across Estrous Cycle Phases.Neuroinformatics · 2026Article
- 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
- VENturing into machine learning for the morphological analysis of von Economo neurons.Scientific reports · 2025Article
- Astrocyte ensheathment of calyx-forming axons of the auditory brainstem precedes accelerated expression of myelin genes and myelination by oligodendrocytes.The Journal of comparative neurology · 2024Article
- Multi-level feature fusion network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Multi-gate Weighted Fusion Network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Sensitivity of CNN image analysis to multifaceted measurements of neurite growth.BMC bioinformatics · 2023Article
- Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M-type classification.Scientific reports · 2023Article
- Polymer Physics-Based Classification of Neurons.Neuroinformatics · 2023Article
- Latent diffusion augmentation enhances deep learning analysis of neuro-morphology in limbal stem cell deficiency.Frontiers in medicine · 2023Article
- Olfactory responses ofeLife · 2022Article
- Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.Molecules (Basel, Switzerland) · 2022Article
- NeuriteNet: A convolutional neural network for assessing morphological parameters of neurite growth.Journal of neuroscience methods · 2021Article
- The roles of supervised machine learning in systems neuroscience.Progress in neurobiology · 2019Review
- Quantifying How Staining Methods Bias Measurements of Neuron Morphologies.Frontiers in neuroinformatics · 2019Article
- Towards a supervised classification of neocortical interneuron morphologies.BMC bioinformatics · 2018Article
- Mining Big Neuron Morphological Data.Computational intelligence and neuroscience · 2018Review
- Image-Based Profiling of Synaptic Connectivity in Primary Neuronal Cell Culture.Frontiers in neuroscience · 2018Review
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4 authors.
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Abstract
Classification and quantitative characterization of neuronal morphologies from histological neuronal reconstruction is challenging since it is still unclear how to delineate a neuronal cell class and which are the best features to define them by. The morphological neuron characterization represents a primary source to address anatomical comparisons, morphometric analysis of cells, or brain modeling. The objectives of this paper are (i) to develop and integrate a pipeline that goes from morphological feature extraction to classification and (ii) to assess and compare the accuracy of machine learning algorithms to classify neuron morphologies. The algorithms were trained on 430 digitally reconstructed neurons subjectively classified into layers and/or m-types using young and/or adult development state population of the somatosensory cortex in rats. For supervised algorithms, linear discriminant analysis provided better classification results in comparison with others. For unsupervised algorithms, the affinity propagation and the Ward algorithms provided slightly better results.
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