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  Dynamic analysis of synaptic loss and synaptic compensation in the process of associative memory ability decline in Alzheimer’s disease

Wang, W., He, C., Wang, Z., Hramov, A., Fan, D., Yuan, M., Luo, X., Kurths, J. (2021): Dynamic analysis of synaptic loss and synaptic compensation in the process of associative memory ability decline in Alzheimer’s disease. - Applied Mathematics and Computation, 408, 126372.
https://doi.org/10.1016/j.amc.2021.126372

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 Creators:
Wang, Weiping1, Author
He, Chang1, Author
Wang, Zhen1, Author
Hramov, Alexander1, Author
Fan, Denggui1, Author
Yuan, Manman1, Author
Luo, Xiong1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The cognitive decline caused by Alzheimer’s disease (AD) has a great impact on the life of patients and their families. Modern medicine has shown that loss of synaptic function is one of the causes of AD, and synaptic compensation compensates for cognitive abilities of the human brain. However, there are no studies on the internal mechanism of synaptic loss and synaptic compensation affecting human cognitive ability. In order to solve this problem, we propose here a three-layer neural network with multiple associative memory abilities, which is one of the main cognitive abilities. Based on synaptic plasticity, models of synaptic loss and synaptic compensation are established to study the pathogenesis of the degeneration of associative memory and explore feasible treatment approaches by setting different degrees of loss and compensation. Our simulation results show that the model can describe the associative memory ability at different stages of AD, which is of great significance for paramedics to determine the stage of disease and develop effective treatment strategies.

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 Dates: 2021-11
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.amc.2021.126372
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Health
Model / method: Nonlinear Data Analysis
 Degree: -

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Title: Applied Mathematics and Computation
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 408 Sequence Number: 126372 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/applied-mathematics-computation
Publisher: Elsevier