These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

115 related articles for article (PubMed ID: 37549118)

  • 1. Identification of two-neuron FitzHugh-Nagumo model based on the speed-gradient and filtering.
    Rybalko A; Fradkov A
    Chaos; 2023 Aug; 33(8):. PubMed ID: 37549118
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Parameter estimation of the FitzHugh-Nagumo model using noisy measurements for membrane potential.
    Che Y; Geng LH; Han C; Cui S; Wang J
    Chaos; 2012 Jun; 22(2):023139. PubMed ID: 22757546
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Estimating the Parameters of Fitzhugh-Nagumo Neurons from Neural Spiking Data.
    Doruk RO; Abosharb L
    Brain Sci; 2019 Dec; 9(12):. PubMed ID: 31835351
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Dynamic behaviors of the FitzHugh-Nagumo neuron model with state-dependent impulsive effects.
    He Z; Li C; Chen L; Cao Z
    Neural Netw; 2020 Jan; 121():497-511. PubMed ID: 31655446
    [TBL] [Abstract][Full Text] [Related]  

  • 5. The Fitzhugh-Nagumo model: Firing modes with time-varying parameters & parameter estimation.
    Faghih RT; Savla K; Dahleh MA; Brown EN
    Annu Int Conf IEEE Eng Med Biol Soc; 2010; 2010():4116-9. PubMed ID: 21096631
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Inhibitor-Induced Wavetrains and Spiral Waves in an Extended FitzHugh-Nagumo Model of Nerve Cell Dynamics.
    Gani MO; Kabir MH; Ogawa T
    Bull Math Biol; 2022 Nov; 84(12):145. PubMed ID: 36350426
    [TBL] [Abstract][Full Text] [Related]  

  • 7. "Traveling wave" solutions of FitzHugh model with cross-diffusion.
    Berezovskaya F; Camacho E; Wirkus S; Karev G
    Math Biosci Eng; 2008 Apr; 5(2):239-60. PubMed ID: 18613732
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Robust Adaptive Synchronization of Ring Configured Uncertain Chaotic FitzHugh-Nagumo Neurons under Direction-Dependent Coupling.
    Iqbal M; Rehan M; Hong KS
    Front Neurorobot; 2018; 12():6. PubMed ID: 29535622
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Stochastic differential equations as a tool to regularize the parameter estimation problem for continuous time dynamical systems given discrete time measurements.
    Leander J; Lundh T; Jirstrand M
    Math Biosci; 2014 May; 251():54-62. PubMed ID: 24631177
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Reconstructing parameters of the FitzHugh-Nagumo system from boundary potential measurements.
    He Y; Keyes DE
    J Comput Neurosci; 2007 Oct; 23(2):251-64. PubMed ID: 17492372
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Suppression and frequency control of repetitive spiking in the FitzHugh-Nagumo model.
    Sakaguchi H; Yamasaki K
    Phys Rev E; 2023 Jul; 108(1-1):014207. PubMed ID: 37583215
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Synchronization of coupled different chaotic FitzHugh-Nagumo neurons with unknown parameters under communication-direction-dependent coupling.
    Iqbal M; Rehan M; Khaliq A; Saeed-ur-Rehman ; Hong KS
    Comput Math Methods Med; 2014; 2014():367173. PubMed ID: 25101140
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model.
    Jensen AC; Ditlevsen S; Kessler M; Papaspiliopoulos O
    Phys Rev E Stat Nonlin Soft Matter Phys; 2012 Oct; 86(4 Pt 1):041114. PubMed ID: 23214536
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Inferential framework for nonstationary dynamics. II. Application to a model of physiological signaling.
    Duggento A; Luchinsky DG; Smelyanskiy VN; Khovanov I; McClintock PV
    Phys Rev E Stat Nonlin Soft Matter Phys; 2008 Jun; 77(6 Pt 1):061106. PubMed ID: 18643216
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The Study for Synchronization between Two Coupled FitzHugh-Nagumo Neurons Based on the Laplace Transform and the Adomian Decomposition Method.
    Zhen B; Song Z
    Neural Plast; 2021; 2021():6657835. PubMed ID: 33981336
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Moving data window-based partially-coupled estimation approach for modeling a dynamical system involving unmeasurable states.
    Cui T; Ding F; Hayat T
    ISA Trans; 2022 Sep; 128(Pt B):437-452. PubMed ID: 34916031
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Global dynamics and stochastic resonance of the forced FitzHugh-Nagumo neuron model.
    Gong PL; Xu JX
    Phys Rev E Stat Nonlin Soft Matter Phys; 2001 Mar; 63(3 Pt 1):031906. PubMed ID: 11308677
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Effects of Lévy noise on the Fitzhugh-Nagumo model: A perspective on the maximal likely trajectories.
    Cai R; He Z; Liu Y; Duan J; Kurths J; Li X
    J Theor Biol; 2019 Nov; 480():166-174. PubMed ID: 31419442
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Broad range of neural dynamics from a time-varying FitzHugh-Nagumo model and its spiking threshold estimation.
    Faghih RT; Savla K; Dahleh MA; Brown EN
    IEEE Trans Biomed Eng; 2012 Mar; 59(3):816-23. PubMed ID: 22186931
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Assessment on selectivity of multi-contact cuff electrode for recording peripheral nerve signals using Fitzhugh-Nagumo model of nerve excitation.
    Taghipour-Farshi H; Frounchi J; Ahmadiasl N; Shahabi P; Salekzamani Y
    J Back Musculoskelet Rehabil; 2016 Nov; 29(4):749-756. PubMed ID: 26966830
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.