A Fast Markovian Method For Modeling Channel Noise In Neurons

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.

Güler and Linaro et al Model in an Investigation of the Neuronal Dynamics using noise Comparative Study

Recently, theoretical arguments, numerical simulation and experiments shown that ion channel noise in neurons can have deep impact on the behavior of the neuron's dynamical when there is a limited size for the membrane space. It can be create different models of Linaro al equations by using stochastic differential equations to find the impacts of ion channel noise, and it has been analytically put forward the Güler model. More recently, Güler has discussed that in small neurons the rate functions for the closing and opening of gates are under the effect of the noise. In this research, the investigation of dynamics neurons are determined with noise rate functions. The exact Markov simulations will be employ during the investigation with above analytical models. Comparatively, the results will be presented from these models. The research aims to show more details on the phenomenon recently outlined by Güler.

An investigation of the coefficient of variation using voltage clamps techniques

<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>