An evolutionary developmental approach for generation of 3D neuronal morphologies using gene regulatory networks

Lin, Xianghong; Li, Zhiqiang; Ma, Huifang; Wang, Xiangwen

doi:10.1016/j.neucom.2017.08.005

Cited by 5 publications

(1 citation statement)

References 44 publications

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“…The potential spike pattern of the neuronal cells can be obtained in response to their puncture by a microelectrode; however, it is more complicated to distinguish the types of neurons by the spike firing characteristic of neuronal cells [6]. Dendritic and axonal trees stemming from the soma develop an extremely complex branching pattern that occupies and fills three-dimensional (3D) space [7]. The 3D morphological structure of neurons can be obtained by two-photon excitation fluorescence microscopy and fluorescence staining techniques.…”

Section: Introductionmentioning

confidence: 99%

A Neuronal Morphology Classification Approach Based on Locally Cumulative Connected Deep Neural Networks

Lin

Zheng

2019

Applied Sciences

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Neurons are the basic building and computational units of the nervous system, and have complex and diverse spatial geometric structures. By solving the neuronal classification problem, we can further understand the characteristics of neurons and the process of information transmission. This paper presents a neuronal morphology classification approach based on locally cumulative connected deep neural networks, where 43 geometric features were extracted from two different neuron datasets and applied to classify types of neurons. Then, the effects of different parameters of deep learning networks on the performance of neuron classification were analyzed including mini-batch size, number of intermediate layers, and number of building blocks. The accuracy of the approach was also compared with that of the other mainstream machine learning approaches. The experimental results showed that the proposed approach is effective for solving complex neuronal morphology classification problems.

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Section: Introductionmentioning

confidence: 99%

A Neuronal Morphology Classification Approach Based on Locally Cumulative Connected Deep Neural Networks

Lin

Zheng

2019

Applied Sciences

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A Neuronal Morphology Classification Approach Based on Deep Residual Neural Networks

Lin

Zheng

Wang

et al. 2018

Neural Information Processing

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Machine learning accelerates the technological development of brain science

MengYa

2021

J. Phys.: Conf. Ser.

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The development of machine learning provides a strong research foundation for the field of brain science. Compared with traditional research methods, the strategy based on machine learning is more suitable for the big data background of the current development of the field, and has a broad application prospect. This paper introduces the application status of machine learning in brain science research from three aspects: neuron reconstruction, neuron classification, neuron synthesis, and summarizes the development direction of machine learning technology in the field.

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An evolutionary developmental approach for generation of 3D neuronal morphologies using gene regulatory networks

Cited by 5 publications

References 44 publications

A Neuronal Morphology Classification Approach Based on Locally Cumulative Connected Deep Neural Networks

A Neuronal Morphology Classification Approach Based on Locally Cumulative Connected Deep Neural Networks

A Neuronal Morphology Classification Approach Based on Deep Residual Neural Networks

Machine learning accelerates the technological development of brain science

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