Turing instability mechanism of short-memory formation in multilayer FitzHugh-Nagumo network

被引:0
作者
Wang, Junjie [1 ]
Shen, Jianwei [2 ]
机构
[1] Zhengzhou Univ, Sch Math & Stat, Zhengzhou, Peoples R China
[2] North China Univ Water Resources & Elect Power, Sch Math & Stat, Zhengzhou, Peoples R China
来源
FRONTIERS IN PSYCHIATRY | 2023年 / 14卷
基金
中国国家自然科学基金;
关键词
FHN model; short-term memory; multilayer network; Turing pattern; delay; Hopf bifurcation; noise; PATTERN-FORMATION; HOPF-BIFURCATION; DELAY; MODEL; DIFFUSION; DYNAMICS;
D O I
10.3389/fpsyt.2023.1083015
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
IntroductionThe study of brain function has been favored by scientists, but the mechanism of short-term memory formation has yet to be precise. Research problemSince the formation of short-term memories depends on neuronal activity, we try to explain the mechanism from the neuron level in this paper. Research contents and methodsDue to the modular structures of the brain, we analyze the pattern properties of the FitzHugh-Nagumo model (FHN) on a multilayer network (coupled by a random network). The conditions of short-term memory formation in the multilayer FHN model are obtained. Then the time delay is introduced to more closely match patterns of brain activity. The properties of periodic solutions are obtained by the central manifold theorem. ConclusionWhen the diffusion coeffcient, noise intensity np, and network connection probability p reach a specific range, the brain forms a relatively vague memory. It is found that network and time delay can induce complex cluster dynamics. And the synchrony increases with the increase of p. That is, short-term memory becomes clearer.
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页数:13
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