Machine learning of synaptic structure with neurons to promote tumor growth
| Autor: | Chang Shu, Liancun Zheng, Xuelan Zhang, Erhui Wang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Spiking neural network
Tumor microenvironment Artificial neural network Computer science business.industry Applied Mathematics Mechanical Engineering Machine learning computer.software_genre Human health medicine.anatomical_structure Mechanics of Materials Cancer cell medicine Excitatory postsynaptic potential Tumor growth Neuron Artificial intelligence business computer |
| Zdroj: | Applied Mathematics and Mechanics. 41:1697-1706 |
| ISSN: | 1573-2754 0253-4827 |
| DOI: | 10.1007/s10483-020-2656-8 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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