Pattern Dynamics in a Predator‐Prey Model with Diffusion Network

Yang, Wenjie; Zheng, Qianqian; Shen, Jianwei; Hu, Qingjun

doi:10.1155/2022/9055480

Cited by 1 publication

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“…Qu and Zhang studied the conditions required for various bifurcations in the FHN diffusion system under Neumann boundary conditions and extended them to coupled FHN model ( 17 ). With the boom of complex networks in recent years, many scholars have begun to study the Turing pattern under the network ( 18 – 22 ). Ren et al extended these studies to multilayer networks ( 23 , 24 ).…”

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confidence: 99%

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

Wang

Shen

2023

Front. Psychiatry

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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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