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 *

190 related articles for article (PubMed ID: 30764741)

  • 1. Biologically Realistic Mean-Field Models of Conductance-Based Networks of Spiking Neurons with Adaptation.
    Volo MD; Romagnoni A; Capone C; Destexhe A
    Neural Comput; 2019 Apr; 31(4):653-680. PubMed ID: 30764741
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models.
    Carlu M; Chehab O; Dalla Porta L; Depannemaecker D; Héricé C; Jedynak M; Köksal Ersöz E; Muratore P; Souihel S; Capone C; Zerlaut Y; Destexhe A; di Volo M
    J Neurophysiol; 2020 Mar; 123(3):1042-1051. PubMed ID: 31851573
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Self-sustained asynchronous irregular states and Up-Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons.
    Destexhe A
    J Comput Neurosci; 2009 Dec; 27(3):493-506. PubMed ID: 19499317
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons.
    Zerlaut Y; Chemla S; Chavane F; Destexhe A
    J Comput Neurosci; 2018 Feb; 44(1):45-61. PubMed ID: 29139050
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A novel density-based neural mass model for simulating neuronal network dynamics with conductance-based synapses and membrane current adaptation.
    Huang CH; Lin CK
    Neural Netw; 2021 Nov; 143():183-197. PubMed ID: 34157643
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation.
    Augustin M; Ladenbauer J; Baumann F; Obermayer K
    PLoS Comput Biol; 2017 Jun; 13(6):e1005545. PubMed ID: 28644841
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Exact mean-field models for spiking neural networks with adaptation.
    Chen L; Campbell SA
    J Comput Neurosci; 2022 Nov; 50(4):445-469. PubMed ID: 35834100
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A master equation formalism for macroscopic modeling of asynchronous irregular activity states.
    El Boustani S; Destexhe A
    Neural Comput; 2009 Jan; 21(1):46-100. PubMed ID: 19210171
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Contributions of intrinsic membrane dynamics to fast network oscillations with irregular neuronal discharges.
    Geisler C; Brunel N; Wang XJ
    J Neurophysiol; 2005 Dec; 94(6):4344-61. PubMed ID: 16093332
    [TBL] [Abstract][Full Text] [Related]  

  • 10. How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?
    Grabska-Barwińska A; Latham PE
    J Comput Neurosci; 2014 Jun; 36(3):469-81. PubMed ID: 24091644
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Biophysically grounded mean-field models of neural populations under electrical stimulation.
    Cakan C; Obermayer K
    PLoS Comput Biol; 2020 Apr; 16(4):e1007822. PubMed ID: 32324734
    [TBL] [Abstract][Full Text] [Related]  

  • 12. State-dependent mean-field formalism to model different activity states in conductance-based networks of spiking neurons.
    Capone C; di Volo M; Romagnoni A; Mattia M; Destexhe A
    Phys Rev E; 2019 Dec; 100(6-1):062413. PubMed ID: 31962518
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models.
    Alexandersen CG; Duprat C; Ezzati A; Houzelstein P; Ledoux A; Liu Y; Saghir S; Destexhe A; Tesler F; Depannemaecker D
    Neural Comput; 2024 Jun; 36(7):1433-1448. PubMed ID: 38776953
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Including long-range dependence in integrate-and-fire models of the high interspike-interval variability of cortical neurons.
    Jackson BS
    Neural Comput; 2004 Oct; 16(10):2125-95. PubMed ID: 15333210
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Resonance or integration? Self-sustained dynamics and excitability of neural microcircuits.
    Muresan RC; Savin C
    J Neurophysiol; 2007 Mar; 97(3):1911-30. PubMed ID: 17135469
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Mean-field equations for neuronal networks with arbitrary degree distributions.
    Nykamp DQ; Friedman D; Shaker S; Shinn M; Vella M; Compte A; Roxin A
    Phys Rev E; 2017 Apr; 95(4-1):042323. PubMed ID: 28505854
    [TBL] [Abstract][Full Text] [Related]  

  • 17. The high-conductance state of cortical networks.
    Kumar A; Schrader S; Aertsen A; Rotter S
    Neural Comput; 2008 Jan; 20(1):1-43. PubMed ID: 18044999
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Dynamics of the exponential integrate-and-fire model with slow currents and adaptation.
    Barranca VJ; Johnson DC; Moyher JL; Sauppe JP; Shkarayev MS; Kovačič G; Cai D
    J Comput Neurosci; 2014 Aug; 37(1):161-80. PubMed ID: 24443127
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Mean-driven and fluctuation-driven persistent activity in recurrent networks.
    Renart A; Moreno-Bote R; Wang XJ; Parga N
    Neural Comput; 2007 Jan; 19(1):1-46. PubMed ID: 17134316
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Macroscopic dynamics of neural networks with heterogeneous spiking thresholds.
    Gast R; Solla SA; Kennedy A
    Phys Rev E; 2023 Feb; 107(2-1):024306. PubMed ID: 36932598
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.