A simple Markov model of sodium channels with a dynamic threshold
- 462 Downloads
Characteristics of action potential generation are important to understanding brain functioning and, thus, must be understood and modeled. It is still an open question what model can describe concurrently the phenomena of sharp spike shape, the spike threshold variability, and the divisive effect of shunting on the gain of frequency-current dependence. We reproduced these three effects experimentally by patch-clamp recordings in cortical slices, but we failed to simulate them by any of 11 known neuron models, including one- and multi-compartment, with Hodgkin-Huxley and Markov equation-based sodium channel approximations, and those taking into account sodium channel subtype heterogeneity. Basing on our voltage-clamp data characterizing the dependence of sodium channel activation threshold on history of depolarization, we propose a 3-state Markov model with a closed-to-open state transition threshold dependent on slow inactivation. This model reproduces the all three phenomena. As a reduction of this model, a leaky integrate-and-fire model with a dynamic threshold also shows the effect of gain reduction by shunt. These results argue for the mechanism of gain reduction through threshold dynamics determined by the slow inactivation of sodium channels.
KeywordsDivisive effect Spike shape Spike threshold Sodium channels Conductance-based neurons Dynamic patch-clamp
The reported study was supported by RFBR, research projects 11-04-01281a and 13-04-01835a.
Conflict of interest
The authors declare that they have no conflict of interest.
- Chizhov, A. V. (2013). Conductance-based refractory density model of primary visual cortex. Journal of Computational Neuroscience PMID: 23888313 (Epub ahead of print). http://link.springer.com/article/10.1007%2Fs10827-013-0473-5
- Chizhov A.V., Smirnova E.Yu., Karabasov I.N., Simonov A.Yu., Marinazzo D., Schramm A., Graham L.J. (2011). Spike thresholds dynamics explains the ability of a neuron to divide. Proceedings of the conference. Neuroinformatics, 2, 205–213.Google Scholar
- Graham, L. J., & Schramm, A. (2009). In vivo dynamic clamp: The functional impact of synaptic and intrinsic conductances in visual cortex. In A. Destexhe, & T. Bal (Eds.) Dynamic clamp: From principles to applications. Springer.Google Scholar
- McCormick, D. A., Shu, Y., & Yu, Y. (2007). Neurophysiology: Hodgkin and Huxley model–still standing? Nature, 445(E1–2), discussion E2–3.Google Scholar