Modeling neuronal activity in relation to experimental voltage-/patch-clamp recordings

A mechanism-based, Hodgkin-Huxley-type modeling approach is proposed that allows connecting the key parameters of experimental voltage-/patch-clamp data directly to the major control values of the model. The objective of this paper is to facilitate the use of mathematical modeling in supplement to electrophysiological recordings. Typical recordings from current-clamp, whole-cell voltage-clamp, and single-channel patch-clamp experiments are illustrated by means of a simplified computer model designed for life science education. These examples demonstrate that the "rate constants", on which the original Hodgkin-Huxley equations are built up, are difficult, in most experiments even impossible, to extract from experimental data. As the combination of the two exponential rate constants leads to sigmoid activation curves, they can be replaced by sigmoid voltage dependencies, mostly presented in form of Boltzmann functions. Conversely, connecting whole-cell and single-channel patch-clamp simulations, the Boltzmann functions, can be related to exponentially voltage dependent probability factors of ion channel transition rates. The thereby introduced small variability of the activation values suggests that the power functions of the activation variables in the current equations can be neglected. Eliminating the rate constants and the power functions can be physiologically justified and makes the model easier to handle, especially in context with experimental data. Further possibilities of dimension reduction as well as model extensions are discussed. This article is part of a Special Issue entitled Neural Coding 2012.

[1]  S. Postnova A mathematical model of sleep-wake cycles: the role of hypocretin/orexin in homeostatic regulation and thalamic synchronization , 2010 .

[2]  Svetlana Postnova,et al.  Propagation effects of current and conductance noise in a model neuron with subthreshold oscillations. , 2008, Mathematical biosciences.

[3]  Frank Moss,et al.  Noise-induced precursors of tonic-to-bursting transitions in hypothalamic neurons and in a conductance-based model. , 2011, Chaos.

[4]  Hans A Braun,et al.  Neural Synchronization at Tonic-to-Bursting Transitions , 2007, Journal of biological physics.

[5]  Martin Tobias Huber,et al.  Computer Simulations of Neuronal Signal Transduction: The Role of Nonlinear Dynamics and Noise , 1998 .

[6]  J. Hindmarsh,et al.  A model of neuronal bursting using three coupled first order differential equations , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[7]  R. FitzHugh Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.

[8]  Frank Moss,et al.  Noisy activation kinetics induces bursting in the Huber-Braun neuron model , 2010 .

[9]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990 .

[10]  H A Braun,et al.  Neurones and synapses for systemic models of psychiatric disorders. , 2010, Pharmacopsychiatry.

[11]  B. Hille Ionic channels of excitable membranes , 2001 .

[12]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[13]  B. Sakmann,et al.  The patch clamp technique. , 1992, Scientific American.

[14]  E Mosekilde,et al.  Bifurcation structure of a model of bursting pancreatic cells. , 2001, Bio Systems.

[15]  C. Belmonte,et al.  Role of Ih in the firing pattern of mammalian cold thermoreceptor endings. , 2012, Journal of neurophysiology.

[16]  D. Noble A modification of the Hodgkin—Huxley equations applicable to Purkinje fibre action and pacemaker potentials , 1962, The Journal of physiology.

[17]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.