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

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  • Wang, Weiping
  • He, Chang
  • Wang, Zhen
  • Hramov, Alexander
  • Fan, Denggui
  • Yuan, Manman
  • Luo, Xiong
  • Kurths, Jürgen

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.

Suggested Citation

  • Wang, Weiping & He, Chang & Wang, Zhen & Hramov, Alexander & Fan, Denggui & Yuan, Manman & Luo, Xiong & Kurths, Jürgen, 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, Elsevier, vol. 408(C).
    • Handle: RePEc:eee:apmaco:v:408:y:2021:i:c:s0096300321004616
    DOI: 10.1016/j.amc.2021.126372
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    References listed on IDEAS

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    1. Xiaofan Li & Yuan Ge & Hongjian Liu & Huiyuan Li & Jian-an Fang, 2020. "New Results on Synchronization of Fractional-Order Memristor‐Based Neural Networks via State Feedback Control," Complexity, Hindawi, vol. 2020, pages 1-11, September.
    2. Kafraj, Mohadeseh Shafiei & Parastesh, Fatemeh & Jafari, Sajad, 2020. "Firing patterns of an improved Izhikevich neuron model under the effect of electromagnetic induction and noise," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    3. Mohadeseh Shafiei & Fatemeh Parastesh & Mahdi Jalili & Sajad Jafari & Matjaž Perc & Mitja Slavinec, 2019. "Effects of partial time delays on synchronization patterns in Izhikevich neuronal networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 92(2), pages 1-7, February.
    4. Wang, Zhen & Parastesh, Fatemeh & Rajagopal, Karthikeyan & Hamarash, Ibrahim Ismael & Hussain, Iqtadar, 2020. "Delay-induced synchronization in two coupled chaotic memristive Hopfield neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    5. Wang, Weiping & Jia, Xiao & Luo, Xiong & Kurths, Jürgen & Yuan, Manman, 2019. "Fixed-time synchronization control of memristive MAM neural networks with mixed delays and application in chaotic secure communication," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 85-96.
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    Cited by:

    1. Li, Huixia & Zhao, Hongyong, 2022. "Mathematical model of Alzheimer’s disease with prion proteins interactions and treatment," Applied Mathematics and Computation, Elsevier, vol. 433(C).

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