Evidence map›Paper›PMID 33981336›Full record

ArticleNeural plasticity2021

The Study for Synchronization between Two Coupled FitzHugh-Nagumo Neurons Based on the Laplace Transform and the Adomian Decomposition Method.

Bin Zhen, Zigen Song

Abstract read
In one paragraph

Article in Neural plasticity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Bin ZhenSchool of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China.
Zigen SongCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, China.ORCID 0000-0002-5790-7901

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The synchronization between two coupled FitzHugh-Nagumo (FHN) neurons with or without external current is studied by using the Laplace transform and the Adomian decomposition method. Different from other researches, the synchronization error system is expressed as sets of Volterra integral equations based on the convolution theorem in the Laplace transform. Then, it is easy to analytically obtain the conditions that synchronization errors disappear based on the successive approximation method in integral equation theorem, the correctness of which is verified by numerical simulations. Furthermore, the synchronous dynamics of the two coupled FHN neurons also can be written in the form of Volterra integral equations, which is more convenient to analytically solve by using the Adomian decomposition method. It is found that the occurrence of synchronization between the two FHN neurons only depends on the coupling strength and is irrelevant to the external current. Only synchronous rest state in the two FHN neurons without external current can be achieved, while synchronous spikes appear if the external current is not zero.

Indexed as

Computer SimulationAlgorithmsCortical SynchronizationHumansModels, NeurologicalNeuronsNonlinear Dynamics

Identifiers

PMID33981336
PMCPMC8088359

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