Stochastic Nonlinear Equations Describing the Mesoscopic Voltage-Gated Ion Channels
Corresponding Author
Mauricio Tejo
Centro de Análisis Estocástico, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, 7820436 Santiago, Chile uc.cl
Facultad de Ciencias Naturales y Exactas, Universidad de Playa Ancha, Leopoldo Carvallo 270, 2360696 Valparaíso, Chile upla.cl
Search for more papers by this authorCorresponding Author
Mauricio Tejo
Centro de Análisis Estocástico, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, 7820436 Santiago, Chile uc.cl
Facultad de Ciencias Naturales y Exactas, Universidad de Playa Ancha, Leopoldo Carvallo 270, 2360696 Valparaíso, Chile upla.cl
Search for more papers by this authorAbstract
We propose a stochastic nonlinear system to model the gating activity coupled with the membrane potential for a typical neuron. It distinguishes two different levels: a macroscopic one, for the membrane potential, and a mesoscopic one, for the gating process through the movement of its voltage sensors. Such a nonlinear system can be handled to form a Hodgkin-Huxley-like model, which links those two levels unlike the original deterministic Hodgkin-Huxley model which is positioned at a macroscopic scale only. Also, we show that an interacting particle system can be used to approximate our model, which is an approximation technique similar to the jump Markov processes, used to approximate the original Hodgkin-Huxley model.
References
- 1 Hodgkin A. L. and Huxley A. F., A quantitative description of membrane current and its application to conduction and excitation in nerve., The Journal of Physiology. (1952) 117, no. 4, 500–544, 2-s2.0-35649001607, https://doi.org/10.1113/jphysiol.1952.sp004764.
- 2 DorvalA. D.Jr. and White J. A., Channel noise is essential for perithreshold oscillations in entorhinal stellate neurons, The Journal of Neuroscience. (2005) 25, no. 43, 10025–10028, https://doi.org/10.1523/jneurosci.3557-05.2005, 2-s2.0-27344452348.
- 3 Fox R. F., Stochastic versions of the Hodgkin-Huxley equations, Biophysical Journal. (1997) 72, no. 5, 2068–2074, https://doi.org/10.1016/S0006-3495(97)78850-7, 2-s2.0-0030893069.
- 4 Fox R. F. and Lu Y.-N., Emergent collective behavior in large numbers of globally coupled independently stochastic ion channels, Physical Review E. (1994) 49, no. 4, 3421–3431, https://doi.org/10.1103/PhysRevE.49.3421, 2-s2.0-0001212640.
- 5 Austin T. D., The emergence of the deterministic Hodgkin-Huxley equations as a limit from the underlying stochastic ion-channel mechanism, The Annals of Applied Probability. (2008) 18, no. 4, 1279–1325, https://doi.org/10.1214/07-aap494, MR2434172, 2-s2.0-52949122900.
- 6 Pakdaman K., Thieullen M., and Wainrib G., Fluid limit theorems for stochastic hybrid systems with application to neuron models, Advances in Applied Probability. (2010) 42, no. 3, 761–794, 2-s2.0-78650962578, https://doi.org/10.1239/aap/1282924062, MR2779558.
- 7 Bezanilla F., The voltage sensor in voltage-dependent ion channels, Physiological Reviews. (2000) 80, no. 2, 555–592, 2-s2.0-0034017867.
- 8 McKean H., Propagation of chaos for a class of non-linear parabolic equations, Stochastic Differential Equations (Lecture Series in Differential Equations, Session 7, Catholic University, 1967), 1967, 41–57.
- 9
Funaki T., A certain class of diffusion processes associated with nonlinear parabolic equations, Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete. (1984) 67, no. 3, 331–348, https://doi.org/10.1007/bf00535008, MR762085, 2-s2.0-0009088163.
10.1007/BF00535008 Google Scholar
- 10
Sznitman A.-S., P.-L. Hennequin, Topics in propagation of chaos, Ecole d′Eté de Probabilités de Saint-Flour XIX—1989, 1991, 1464, Springer, Berlin, Germany, 165–251, Lecture Notes in Mathematics, https://doi.org/10.1007/BFb0085169.
10.1007/BFb0085169 Google Scholar
- 11
Méléard S., Asymptotic behaviour of some interacting particle systems; McKean-Vlasov and Boltzmann models, Probabilistic Models for Nonlinear Partial Differential Equations, 1996, 1627, Springer, Berlin, Germany, 42–95, Lecture Notes in Mathematics, https://doi.org/10.1007/BFb0093177.
10.1007/BFb0093177 Google Scholar
- 12 Bossy M. and Talay D., A stochastic particle method for the McKean-Vlasov and the Burgers equation, Mathematics of Computation. (1997) 66, no. 217, 157–192, 2-s2.0-0031539944, MR1370849, https://doi.org/10.1090/S0025-5718-97-00776-X.
- 13
Tejo M., Modified Hodgkin-Huxley equations arising from mesoscopic voltage-gated processes, Open Systems and Information Dynamics. (2012) 19, no. 2, 18, 1250014, https://doi.org/10.1142/s123016121250014x, 2-s2.0-84861896967.
10.1142/S123016121250014X Google Scholar
- 14
Bossy M. and
Talay D., Convergence rate for the approximation of the limit law of weakly interacting particles: application to the Burgers equation, The Annals of Applied Probability. (1996) 6, no. 3, 818–861, https://doi.org/10.1214/aoap/1034968229, MR1410117.
10.1214/aoap/1034968229 Google Scholar
- 15 Dobrushin R. L., Prescribing a system of random variables by conditional distributions, Theory of Probability & Its Applications. (1970) 15, no. 3, 458–486, https://doi.org/10.1137/1115049.
- 16 Kac M., Foundation of kinetic theory, 3, Proceedings of the 3rd Berkeley Symposium on Mathematical Statistics and Probability, 1956, Berkeley, Calif, USA, 171–197.
- 17 Bezanilla F., Ion channels: from conductance to structure, Neuron. (2008) 60, no. 3, 456–468, https://doi.org/10.1016/j.neuron.2008.10.035, 2-s2.0-55049099359.
- 18
Mao X., Stochastic Differential Equations and Application, 2008, 2nd edition, Horwood, Chichester, UK, 2-s2.0-84855593535, https://doi.org/10.1533/9780857099402, MR2380366.
10.1533/9780857099402 Google Scholar
- 19
Khasminskii R., Stochastic Stability of Differential Equations, 2012, 66, second edition, Springer, Stochastic Modelling and Applied Probability, MR2894052, https://doi.org/10.1007/978-3-642-23280-0.
10.1007/978-3-642-23280-0 Google Scholar
- 20 Chow C. C. and White J. A., Spontaneous action potentials due to channel fluctuations, Biophysical Journal. (1996) 71, no. 6, 3013–3021, https://doi.org/10.1016/S0006-3495(96)79494-8, 2-s2.0-0029750732.
- 21
Thieullen M., M. Bachar, J. Batzel, and S. Ditlevsen, Deterministic and stochastic FitzHugh-Nagumo systems, Stochastic Biomathematical Models, 2013, 2058, Springer, Berlin, Germany, 175–186, Lecture Notes in Mathematics, https://doi.org/10.1007/978-3-642-32157-3_7.
10.1007/978-3-642-32157-3_7 Google Scholar
- 22
Freidlin M. I. and
Wentzell A. D., Random Perturbations of Dynamical Systems, 1998, Springer, https://doi.org/10.1007/978-1-4612-0611-8, MR1652127.
10.1007/978-1-4612-0611-8 Google Scholar
- 23 Izhikevich E. M., Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting, 2007, MIT Press, Computational Neuroscience, MR2263523.
- 24 Davis M. H., Piecewise-deterministic Markov processes: a general class of non-diffusion stochastic models, Journal of the Royal Statistical Society. Series B. Methodological. (1984) 46, no. 3, 353–388, MR790622.
- 25 Buckwar E. and Riedler M. G., An exact stochastic hybrid model of excitable membranes including spatio-temporal evolution, Journal of Mathematical Biology. (2011) 63, no. 6, 1051–1093, https://doi.org/10.1007/s00285-010-0395-z, MR2855804, 2-s2.0-81255149654.