Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks

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...

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Autores principales:	Tielin Zhang, Yi Zeng, Yue Zhang, Xinhe Zhang, Mengting Shi, Likai Tang, Duzhen Zhang, Bo Xu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/06bc5fac959a45db8b2f52b6c7036566
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spelling	oai:doaj.org-article:06bc5fac959a45db8b2f52b6c70365662021-12-02T14:25:22ZNeuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks10.1038/s41598-021-86780-42045-2322https://doaj.org/article/06bc5fac959a45db8b2f52b6c70365662021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86780-4https://doaj.org/toc/2045-2322Abstract 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.Tielin ZhangYi ZengYue ZhangXinhe ZhangMengting ShiLikai TangDuzhen ZhangBo XuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tielin Zhang Yi Zeng Yue Zhang Xinhe Zhang Mengting Shi Likai Tang Duzhen Zhang Bo Xu Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks
description	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.
format	article
author	Tielin Zhang Yi Zeng Yue Zhang Xinhe Zhang Mengting Shi Likai Tang Duzhen Zhang Bo Xu
author_facet	Tielin Zhang Yi Zeng Yue Zhang Xinhe Zhang Mengting Shi Likai Tang Duzhen Zhang Bo Xu
author_sort	Tielin Zhang
title	Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks
title_short	Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks
title_full	Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks
title_fullStr	Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks
title_full_unstemmed	Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks
title_sort	neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/06bc5fac959a45db8b2f52b6c7036566
work_keys_str_mv	AT tielinzhang neurontypeclassificationinratbrainbasedonintegrativeconvolutionalandtreebasedrecurrentneuralnetworks AT yizeng neurontypeclassificationinratbrainbasedonintegrativeconvolutionalandtreebasedrecurrentneuralnetworks AT yuezhang neurontypeclassificationinratbrainbasedonintegrativeconvolutionalandtreebasedrecurrentneuralnetworks AT xinhezhang neurontypeclassificationinratbrainbasedonintegrativeconvolutionalandtreebasedrecurrentneuralnetworks AT mengtingshi neurontypeclassificationinratbrainbasedonintegrativeconvolutionalandtreebasedrecurrentneuralnetworks AT likaitang neurontypeclassificationinratbrainbasedonintegrativeconvolutionalandtreebasedrecurrentneuralnetworks AT duzhenzhang neurontypeclassificationinratbrainbasedonintegrativeconvolutionalandtreebasedrecurrentneuralnetworks AT boxu neurontypeclassificationinratbrainbasedonintegrativeconvolutionalandtreebasedrecurrentneuralnetworks
_version_	1718391350289760256

Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks

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