Machine learning of synaptic structure with neurons to promote tumor growth

doi:10.1007/s10483-020-2656-8

Abstract

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.

Key words: machine learning technique, computational hemodynamics, electrodiffusive activity, complex synaptic dynamics

2010 MSC Number:

62M45

Erhui WANG, Xuelan ZHANG, Liancun ZHENG, Chang SHU. Machine learning of synaptic structure with neurons to promote tumor growth. Applied Mathematics and Mechanics (English Edition), 2020, 41(11): 1697-1706.

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References

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