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Journal Article

Dynamic analysis of disease progression in Alzheimer’s disease under the influence of hybrid synapse and spatially correlated noise

Authors

Wang,  Weiping
External Organizations;

He,  Chang
External Organizations;

Wang,  Zhen
External Organizations;

Cheng,  Jun
External Organizations;

Mo,  Xishuo
External Organizations;

Tian,  Kuo
External Organizations;

Fan,  Denggui
External Organizations;

Luo,  Xiong
External Organizations;

Yuan,  Manman
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

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Citation

Wang, W., He, C., Wang, Z., Cheng, J., Mo, X., Tian, K., Fan, D., Luo, X., Yuan, M., Kurths, J. (2021): Dynamic analysis of disease progression in Alzheimer’s disease under the influence of hybrid synapse and spatially correlated noise. - Neurocomputing, 456, 23-35.
https://doi.org/10.1016/j.neucom.2021.05.067


Cite as: https://publications.pik-potsdam.de/pubman/item/item_26276
Abstract
Alzheimer’s disease (AD), characterized by cognitive impairment, mainly affects middle-aged and elderly people. As the aging process of the world continues to intensify, AD harms people’s life, economy and society more and more seriously. Therefore, it has become an urgent problem to study the pathogenesis of AD and seek treatment on this basis. Hybrid synapse, autapse and spatial correlated noise in diverse neural activities have been investigated separately, however, theoretically understanding combination of them still has not been fully studied. Here in this paper, a neural network with multiple associative memory abilities is established from the perspective of the degeneration of associative memory ability in AD patients under the conditions of hybrid synapse, autapse and spatial correlated noise. In order to explore the pathogenesis, a synaptic loss and synaptic compensation model are established to analyze the associative memory ability of AD in different degrees of disease. The simulation results demonstrate the effectiveness of the proposed models and pave a way in the study of dynamic mechanism with higher bio-interpretability in neural networks.