ReviewTrends in neurosciences2015
Towards the automatic classification of neurons.
Review in Trends in neurosciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
55 citing papers in PubMed.
- Application of Machine Learning Models to Identify Differences in Neural Electrophysiological Properties Across Estrous Cycle Phases.Neuroinformatics · 2026Article
- Organization and Community Usage of a Neuron Type Circuitry Knowledge Base of the Hippocampal Formation.Biomedicines · 2025Article
- Unsupervised classification of brain-wide axons reveals the presubiculum neuronal projection blueprint.Nature communications · 2024Article
- DSM: Deep sequential model for complete neuronal morphology representation and feature extraction.Patterns (New York, N.Y.) · 2024Article
- Multi-level feature fusion network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Betz cells of the primary motor cortex.The Journal of comparative neurology · 2024Review
- Sensitivity of CNN image analysis to multifaceted measurements of neurite growth.BMC bioinformatics · 2023Article
- Unsupervised classification of brain-wide axons reveals neuronal projection blueprint.Research square · 2023Article
- Correlation Analysis of Molecularly-Defined Cortical Interneuron Populations with Morpho-Electric Properties in Layer V of Mouse Neocortex.Neuroscience bulletin · 2023Article
- Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus.Cognitive computation · 2023Article
- Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors.Journal of neuroscience research · 2023Article
- Circuit analysis of theNetwork neuroscience (Cambridge, Mass.) · 2023Article
- RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations.Frontiers in neuroinformatics · 2022Article
- Identification of adult spinal Shox2 neuronal subpopulations based on unbiased computational clustering of electrophysiological properties.Frontiers in neural circuits · 2022Article
- Cell types as species: Exploring a metaphor.Frontiers in plant science · 2022Review
- Efficient metadata mining of web-accessible neural morphologies.Progress in biophysics and molecular biology · 2022Article
- The landscape of regulatory genes in brain-wide neuronal phenotypes of a vertebrate brain.eLife · 2021Article
- An update to Hippocampome.org by integrating single-cell phenotypes with circuit function in vivo.PLoS biology · 2021Article
- Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks.Scientific reports · 2021Article
- Neuronal classification from network connectivity via adjacency spectral embedding.Network neuroscience (Cambridge, Mass.) · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting growth of information about morphological, physiological, and molecular properties encourages efforts to automate neuronal classification by powerful machine learning techniques. We review state-of-the-art analysis approaches and the availability of suitable data and resources, highlighting prominent challenges and opportunities. The effective solution of the neuronal classification problem will require continuous development of computational methods, high-throughput data production, and systematic metadata organization to enable cross-laboratory integration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.