ArticleScientific reports2021
Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 39 citations in OpenAlex.
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- Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex.Neuroinformatics · 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
- Multi-gate Weighted Fusion Network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Morphology and Fractal-Based Classifications of Neurons and Microglia in Two and Three Dimensions.Advances in neurobiology · 2024Review
- Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M-type classification.Scientific reports · 2023Article
- Moderate white light exposure enhanced spatial memory retrieval by activating a central amygdala-involved circuit in mice.Communications biology · 2023Article
- Machine learning classification reveals robust morphometric biomarker of glial and neuronal arbors.Journal of neuroscience research · 2023Article
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- Evolutionarily conservative and non-conservative regulatory networks during primate interneuron development revealed by single-cell RNA and ATAC sequencing.Cell research · 2022Article
- MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks.Frontiers in neuroscience · 2022Article
- Open Frontiers in Neural Cell Type Investigations; Lessons FromFrontiers in neuroscience · 2021Review
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of cellular complexity in the nervous system based on anatomy has shown more practical and objective advantages in morphology than other perspectives on molecular, physiological, and evolutionary aspects. However, morphology-based neuron type classification in the whole rat brain is challenging, given the significant number of neuron types, limited reconstructed neuron samples, and diverse data formats. Here, we report that different types of deep neural network modules may well process different kinds of features and that the integration of these submodules will show power on the representation and classification of neuron types. For SWC-format data, which are compressed but unstructured, we construct a tree-based recurrent neural network (Tree-RNN) module. For 2D or 3D slice-format data, which are structured but with large volumes of pixels, we construct a convolutional neural network (CNN) module. We also generate a virtually simulated dataset with two classes, reconstruct a CASIA rat-neuron dataset with 2.6 million neurons without labels, and select the NeuroMorpho-rat dataset with 35,000 neurons containing hierarchical labels. In the twelve-class classification task, the proposed model achieves state-of-the-art performance compared with other models, e.g., the CNN, RNN, and support vector machine based on hand-designed features.
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