Model Concept: Markov-type model

Markov models are characterized by a collection of states, one of which corresponds to the system at particular time, and each of which represent a (set of) configuration(s) of the receptor or ion-channel protein(s) corresponding to a particular conductance. The states have rate constants between them that may be functions of voltage or chemical concentrations.

  1. A Markov model of human Cav2.3 channels and their modulation by Zn2+ (Neumaier et al 2020)
  2. A model of beta-adrenergic modulation of IKs in the guinea-pig ventricle (Severi et al. 2009)
  3. A neuronal circuit simulator for non Monte Carlo analysis of neuronal noise (Kilinc & Demir 2018)
  4. A single kinetic model for all human voltage-gated sodium channels (Balbi et al, 2017)
  5. Accurate and fast simulation of channel noise in conductance-based model neurons (Linaro et al 2011)
  6. Alcohol action in a detailed Purkinje neuron model and an efficient simplified model (Forrest 2015)
  7. Alcohol excites Cerebellar Golgi Cells by inhibiting the Na+/K+ ATPase (Botta et al.2010)
  8. Application of a common kinetic formalism for synaptic models (Destexhe et al 1994)
  9. BK - CaV coupling (Montefusco et al. 2017)
  10. CA1 pyramidal neuron: Persistent Na current mediates steep synaptic amplification (Hsu et al 2018)
  11. CA1 pyramidal: Stochastic amplification of KCa in Ca2+ microdomains (Stanley et al. 2011)
  12. Hippocampus CA1 pyramidal model with Na channel exhibiting slow inactivation (Menon et al. 2009)
  13. INa and IKv4.3 heterogeneity in canine LV myocytes (Flaim et al 2006)
  14. Information transmission in cerebellar granule cell models (Rossert et al. 2014)
  15. Ion channel modeling with whole cell and a genetic algorithm (Gurkiewicz and Korngreen 2007)
  16. Kinetic properties of voltage gated Na channel (Nayak and Sikdar 2007)
  17. Kinetic synaptic models applicable to building networks (Destexhe et al 1998)
  18. Markov models of SCN1A (NaV1.1) applied to abnormal gating and epilepsy (Clancy and Kass 2004)
  19. Markovian model for cardiac sodium channel (Clancy, Rudy 2002)
  20. Markovian model for single-channel recordings of Ik_1 in ventricular cells (Matsuoka et al 2003)
  21. Maximal firing rate in midbrain dopamine neurons (Knowlton et al., 2021)
  22. Neurophysiological impact of inactivation pathways in A-type K+ channels (Fineberg et al 2012)
  23. Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)
  24. Paradoxical GABA-mediated excitation (Lewin et al. 2012)
  25. Phenomenological models of NaV1.5: Hodgkin-Huxley and kinetic formalisms (Andreozzi et al 2019)
  26. Role of KCNQ1 and IKs in cardiac repolarization (Silva, Rudy 2005)
  27. Role of KCNQ1 and IKs in cardiac repolarization (Silva, Rudy 2005) (XPP)
  28. Simple and accurate Diffusion Approximation algor. for stochastic ion channels (Orio & Soudry 2012)
  29. State dependent drug binding to sodium channels in the dentate gyrus (Thomas & Petrou 2013)
  30. Stochastic automata network Markov model descriptors of coupled Ca2+ channels (Nguyen et al. 2005)
  31. Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011)
  32. Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011) (pylab)
Top authors for Markov-type model:
Top concepts studied with Markov-type model:
Top neurons studied with Markov-type model:
Top currents studied with Markov-type model:
Top references cited by these models:
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