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Machine learning of synaptic structure with neurons to promote tumor growth

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  • 1. School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China;
    2. School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China;
    3. State Key Laboratory of Cardiovascular Disease, Center of Vascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China

Received date: 2020-03-16

  Revised date: 2020-06-29

  Online published: 2020-10-24

Supported by

Project supported by the National Natural Science Foundation of China (Nos. 11772046 and 81870345)

Abstract

In this paper, we use machine learning techniques to form a cancer cell model that displays the growth and promotion of synaptic and electrical signals. Here, such a technique can be applied directly to the spiking neural network of cancer cell synapses. The results show that machine learning techniques for the spiked network of cancer cell synapses have the powerful function of neuron models and potential supervisors for different implementations. The changes in the neural activity of tumor microenvironment caused by synaptic and electrical signals are described. It can be used to cancer cells and tumor training processes of neural networks to reproduce complex spatiotemporal dynamics and to mechanize the association of excitatory synaptic structures which are between tumors and neurons in the brain with complex human health behaviors.

Cite this article

Erhui WANG, Xuelan ZHANG, Liancun ZHENG, Chang SHU . Machine learning of synaptic structure with neurons to promote tumor growth[J]. Applied Mathematics and Mechanics, 2020 , 41(11) : 1697 -1706 . DOI: 10.1007/s10483-020-2656-8

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