Simulations of nerve signal propagation in axons

Purpose In the literature, soliton solutions of the Heimburg–Jackson model have been proposed by Drab et al . (2022), but for the considered models, i.e. Eq.(1) and Eq.(2), the existence of solitons, the dispersion analysis and the pseudospectral method have been studied (Engelbrecht et al. , 2006, 2018, 2020; Tamm et al. , 2017, 2022; Peets et al. , 2013). Therefore, the gap should be filled by this work. Design/methodology/approach When nonlinear terms, dissipative terms and forcing terms are ignored, the system (Eq.(2)) reduces to a single, sixth-order partial differential equation (Tamm et al. , 2022). In this work, our aim is to propose analytical solutions in the explicit form via ansatz-based method. Therefore, the parameter effects in wave profile will be proposed clearly in figures. Findings While progress has been made in signal propagation in nerves, thanks to many experimental studies and theoretical predictions over the last two centuries, the results obtained in this study may answer new questions that arise. Originality/value In the literature, the existence of solitons, dispersion analysis and pseudospectral method have been investigated for the Heimburg-Jackson model (Engelbrecht et al. , 2006, 2018, 2020; Tamm et al. , 2017, 2022; Peets et al. , 2013), and this study fills the gap in soliton solutions. Additionally, when nonlinear terms, dissipative term and forcing terms are ignored, the system (Eq.(2)) reduced to a single equation that is sixth-order partial differential equation (Tamm et al. , 2022). In this work, our aim is to propose analytical solutions in the explicit form via ansatz-based method. Therefore, the parameter effects in wave profile will be proposed clearly in figures.

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Publication Details

Journal
Engineering Computations
Published
2024-10-26
DOI
https://doi.org/10.1108/ec-07-2024-0588
Citations
1
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
0.34
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article

Simulations of nerve signal propagation in axons

Zehra Pınar
1 citations
Engineering Computations
Neural dynamics and brain function
0.34
article

Simulations of nerve signal propagation in axons

Zehra Pınar
article en
1 citations

Abstract

Purpose In the literature, soliton solutions of the Heimburg–Jackson model have been proposed by Drab et al . (2022), but for the considered models, i.e. Eq.(1) and Eq.(2), the existence of solitons, the dispersion analysis and the pseudospectral method have been studied (Engelbrecht et al. , 2006, 2018, 2020; Tamm et al. , 2017, 2022; Peets et al. , 2013). Therefore, the gap should be filled by this work. Design/methodology/approach When nonlinear terms, dissipative terms and forcing terms are ignored, the system (Eq.(2)) reduces to a single, sixth-order partial differential equation (Tamm et al. , 2022). In this work, our aim is to propose analytical solutions in the explicit form via ansatz-based method. Therefore, the parameter effects in wave profile will be proposed clearly in figures. Findings While progress has been made in signal propagation in nerves, thanks to many experimental studies and theoretical predictions over the last two centuries, the results obtained in this study may answer new questions that arise. Originality/value In the literature, the existence of solitons, dispersion analysis and pseudospectral method have been investigated for the Heimburg-Jackson model (Engelbrecht et al. , 2006, 2018, 2020; Tamm et al. , 2017, 2022; Peets et al. , 2013), and this study fills the gap in soliton solutions. Additionally, when nonlinear terms, dissipative term and forcing terms are ignored, the system (Eq.(2)) reduced to a single equation that is sixth-order partial differential equation (Tamm et al. , 2022). In this work, our aim is to propose analytical solutions in the explicit form via ansatz-based method. Therefore, the parameter effects in wave profile will be proposed clearly in figures.

Engineering ComputationsVol. 41(10)
Tekirdağ Namık Kemal University (TR)
Openalex Percentile: Top 39%
Neural dynamics and brain function
0.34
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