A Fast Markovian Method For Modeling Channel Noise In Neurons

Author(s):  
Norbert Ankri ◽  
Dominique Debanne

Abstract Channel noise results from rapid transitions of protein channels from closed to open state and is generally considered as the most dominant source of electrical noise causing membrane-potential fluctuations even in the absence of synaptic inputs. The simulation of a realistic channel noise remains a source of possible error. Although the Markovian method is considered as the golden standard for appropriate description of channel noise, its computation time increasing exponentially with numbers of channels, it is poorly suitable to simulate realistic features. We describe here a novel algorithm for simulating ion channel noise based on Markov chains (MC). Although this new algorithm refers to a Monte-Carlo process, it only needs few random numbers whatever the number of channels involved. Our fast MC (FMC) model does not exhibit the drawbacks due to approximations based on stochastic differential equations. In fact, we show here, that these drawbacks can be highlighted even for a high number of channels.

1975 ◽  
Vol 191 (1105) ◽  
pp. 561-565 ◽  

Glutamate-induced potential changes have been recorded with intracellular electrodes in nerve cells of the squid. The responses are accompanied by small voltage fluctuations which resemble postsynaptic ‘membrane noise’ observed at neuromuscular junctions. Certain limitations are discussed in extending the noise analysis to neurons with multiple synaptic inputs.


1997 ◽  
Vol 77 (4) ◽  
pp. 1697-1715 ◽  
Author(s):  
Edward A. Stern ◽  
Anthony E. Kincaid ◽  
Charles J. Wilson

Stern, Edward A., Anthony E. Kincaid, and Charles J. Wilson. Spontaneous subthreshold membrane potential fluctuations and action potential variability of rat corticostriatal and striatal neurons in vivo. J. Neurophysiol. 77: 1697–1715, 1997. We measured the timing of spontaneous membrane potential fluctuations and action potentials of medial and lateral agranular corticostriatal and striatal neurons with the use of in vivo intracellular recordings in urethan-anesthetized rats. All neurons showed spontaneous subthreshold membrane potential shifts from 7 to 32 mV in amplitude, fluctuating between a hyperpolarized down state and depolarized up state. Action potentials arose only during the up state. The membrane potential state transitions showed a weak periodicity with a peak frequency near 1 Hz. The peak of the frequency spectra was broad in all neurons, indicating that the membrane potential fluctuations were not dominated by a single periodic function. At frequencies >1 Hz, the log of magnitude decreased linearly with the log of frequency in all neurons. No serial dependence was found for up and down state durations, or for the time between successive up or down state transitions, showing that the up and down state transitions are not due to superimposition of noisy inputs onto a single frequency. Monte Carlo simulations of stochastic synaptic inputs to a uniform finite cylinder showed that the Fourier spectra obtained for corticostriatal and striatal neurons are inconsistent with a Poisson-like synaptic input, demonstrating that the up state is not due to an increase in the strength of an unpatterned synaptic input. Frequency components arising from state transitions were separated from those arising from the smaller membrane potential fluctuations within each state. A larger proportion of the total signal was represented by the fluctuations within states, especially in the up state, than was predicted by the simulations. The individual state spectra did not correspond to those of random synaptic inputs, but reproduced the spectra of the up and down state transitions. This suggests that the process causing the state transitions and the process responsible for synaptic input may be the same. A high-frequency periodic component in the up states was found in the majority of the corticostriatal cells in the sample. The average size of the component was not different between neurons injected with QX-314 and control neurons. The high-frequency component was not seen in any of our sample of striatal cells. Corticostriatal and striatal neurons' coefficients of variation of interspike intervals ranged from 1.0 to 1.9. When interspike intervals including a down state were subtracted from the calculation, the coefficient of variation ranged from 0.4 to 1.1, indicating that a substantial proportion of spike interval variance was due to the subthreshold membrane potential fluctuations.


2012 ◽  
Vol 107 (8) ◽  
pp. 2143-2153 ◽  
Author(s):  
Deepankar Mohanty ◽  
Benjamin Scholl ◽  
Nicholas J. Priebe

A common technique used to study the response selectivity of neurons is to measure the relationship between sensory stimulation and action potential responses. Action potentials, however, are only indirectly related to the synaptic inputs that determine the underlying, subthreshold, response selectivity. We present a method to predict membrane potential, the measurable result of the convergence of synaptic inputs, based on spike rate alone and then test its utility by comparing predictions to actual membrane potential recordings from simple cells in primary visual cortex. Using a noise stimulus, we found that spike rate receptive fields were in precise correspondence with membrane potential receptive fields ( R2 = 0.74). On average, spike rate alone could predict 44% of membrane potential fluctuations to dynamic noise stimuli, demonstrating the utility of this method to extract estimates of subthreshold responses. We also found that the nonlinear relationship between membrane potential and spike rate could also be extracted from spike rate data alone by comparing predictions from the noise stimulus with the actual spike rate. Our analysis reveals that linear receptive field models extracted from noise stimuli accurately reflect the underlying membrane potential selectivity and thus represent a method to generate estimates of the underlying average membrane potential from spike rate data alone.


1987 ◽  
Vol 103 (3) ◽  
pp. 283-286
Author(s):  
S. I. Zakharov ◽  
K. Yu. Bogdanov ◽  
A. V. Zaitsev ◽  
L. V. Rozenshtraukh

Cephalalgia ◽  
2000 ◽  
Vol 20 (6) ◽  
pp. 533-537 ◽  
Author(s):  
T Leniger ◽  
M Wiemann ◽  
D Bingmann ◽  
A Hufnagel ◽  
U Bonnet

Clinical studies indicate anti-migraneous efficacy of the probably GABAergic anti-convulsants valproate and gabapentin. For the GABAergic anticonvulsants vigabatrin and tiagabine, studies about antimigraneous efficacy are missing. The aim of this study was to test the GABAergic potency of these drugs in vitro before further clinical studies. Intracellular recordings were obtained from hippocampal pyramidal cells. Spontaneous GABAergic hyperpolarizations (SGH) elicited by 75 μ m 4-aminopyridine were used to test the effect of these drugs on GABA-dependent potentials. Tiagabine (0.1 m m) prolonged the duration of SGH. Furthermore, monophasic SGH turned over into triphasic typical GABAergic membrane potential fluctuations within 20 min. In contrast, valproate, gabapentin, and vigabatrin failed to affect SGH up to 60 min of application. The reason for the fast action of tiagabine on SGH may be caused by a faster increase of synaptic GABA levels compared with other drugs. As migraine therapy benefits from an augmentation of GABA activity, we recommend clinical studies of tiagabine as a fast-acting agent in migraine attacks.


2015 ◽  
Vol 4 (4) ◽  
pp. 364
Author(s):  
Ahmed Mahmood Khudhur ◽  
Ahmed N Abdalla ◽  
Jasni Mohamad Zain ◽  
Hai Tao

<p class="MsoNormal" style="text-align: justify; text-justify: inter-ideograph;"><span style="font-size: 10.0pt;">In recent years, it has been argued and experimentally shown that ion channel noise in neurons can have profound effects on the neuron’s dynamical behavior. Most profoundly, ion channel noise was seen to be able to cause spontaneous firing and stochastic resonance. It has been recently found that a non-trivially persistent cross correlation takes place between the transmembrane voltage fluctuations and the component of open channel fluctuations attributed to gate multiplicity. This non-trivial phenomenon was found to play a major augmentative role for the elevation of excitability and spontaneous firing in the small size cell. In addition, the same phenomenon was found to significantly enhance the spike coherence. In this paper, statistics of the coefficient of variation, to be obtained from the colored stochastic Hodgkin-Huxley equations using voltage clamps techniqueswill be studied. The simulation result shows the coefficient of variation; enhance the agreement with the microscopeinthe case of the noisy currents.</span></p>


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