Search PubMed⌕ Search

Biomedical subjects

M Musila

Publications and source records attributed to M Musila.

8 recordsLinked to original sources
PubMed1997

Metastable associative network models of dream sleep.

Up to the present day, simulations using a neural network model have been carried out under the global inhibition and the uncorrelated noise in order to simulate the dynamics transition of neuronal activities from the flat (slow wave sleep) to the 1/f (dream sleep) power spectral density profile during the sleep cycle in cats. In particular, the metastability of the network attractor is shown to be essential in generating the 1/f fluctuations. Here, the dynamics of neuronal and network activities are analyzed under the correlated noises mimicking a cholinergic drive. Regardless of the network structure, symmetry and asymmetry, the behavior of network activity and the escape time distributions show that the long-lasting autocorrelation of the noise prolongs its prescence in the metastable states. These results and the newly estimated network attractor show that the interplay between the metastability of the network attractor and the noise statistics determines the dynamics of network activity. Our results may be able to provide the novel framework to investigate the function of dreaming in the higher-order brain function.

Journal Article↗
PubMed1996

Computation of first passage time moments for stochastic diffusion processes modelling nerve membrane depolarization.

For further understanding of neural coding, stochastic variability of interspike intervals has been investigated by both experimental and theoretical neuroscientists. In stochastic neuronal models, the interspike interval corresponds to the time period during which the process imitating the membrane potential reaches a threshold for the first time from a reset depolarization. For neurons belonging to complex networks in the brain, stochastic diffusion processes are often used to approximate the time course of the membrane potential. The interspike interval is then viewed as the first passage time for the employed diffusion process. Due to a lack of analytical solution for the related first passage time problem for most diffusion neuronal models, a numerical integration method, which serves to compute first passage time moments on the basis of the Siegert recursive formula, is presented in this paper. For their neurobiological plausibility, the method here is associated with diffusion processes whose state spaces are restricted to finite intervals, but it can also be applied to other diffusion processes and in other (non-neuronal) contexts. The capability of the method is demonstrated in numerical examples and the relation between the integration step, accuracy of calculation and amount of computing time required is discussed.

Action Potentials↗
PubMed1994

On the interspike intervals calculated from diffusion approximations of Stein's neuronal model with reversal potentials.

In this paper we compare a discontinuous model with two diffusion models of a simulated neuron. The mean and coefficient of variation of the interspike intervals were calculated for these three models. The means were found to be shorter for the diffusion approximations than for the discontinuous model for all values of parameters. By increasing the excitation, the difference between firing frequencies generated by the models became smaller. The coefficient of variation (CV) exhibited a relatively close agreement for all three models. The firing patterns for both diffusion models were similar to each other.

Action Potentials↗
PubMed1992

Simulation of a diffusion process with randomly distributed jumps in neuronal context.

In stochastic neuronal models, an interspike interval corresponds to the time interval during which the process imitating the membrane potential reaches a threshold from an initial depolarization. For neurons with an extensive dendritic structure, a stochastic process combining diffusion and discontinuous development of its trajectory is considered a good description of the membrane potential. Due to a lack of analytical solutions of the threshold passage distribution for such a process, a method for computer simulation is introduced here. For the diffusion Ornstein-Uhlenbeck process with exponentially distributed moments of constant jumps a program is given. The relation between the simulation step, accuracy of simulation and amount of computing time required is discussed.

Computer Simulation↗
PubMed1992

Effects of afterhyperpolarization on neuronal firing.

Stein's model for a neuron is studied. This model is modified to take into account the effects of afterhyperpolarization on the neuronal firing. The relative refractory phase, following the absolute one, is modelled by a time-increasing amplitude of postsynaptic potentials and it is also incorporated into the model. Besides the simulation of the model, some theoretical results and approximation methods are derived. Afterhyperpolarization tends to preserve the linearity of the frequency transfer characteristic and it has a limited effect on the moments of the interspike intervals in general. The main effects are seen at high firing rates and in the removal of short intervals in the interspike interval histogram.

Animals↗
PubMed1991

Variable initial depolarization in Stein's neuronal model with synaptic reversal potentials.

The effect of a variable initial value is examined in Stein's stochastic neuronal model with synaptic reversal potentials under the conditions of a constant threshold and a constant input. The moments of the interspike interval distribution are presented as the functions of the initial depolarization which ranges from inhibitory reversal potential to the threshold potential. Normal, exponential and transformed Gamma distributions are tested for the initial value of depolarization. The coefficient of variation is shown to be greater than one when the initial depolarization is sufficiently above the resting level. An interpretation of this result in the terms of spatial facilitation is offered. The effect of a random initial value is found to be most pronounced for the neurons depolarized to a near threshold level.

Animals↗
PubMed1991

Generalized Stein's model for anatomically complex neurons.

A neuron with a large dendritic structure is considered. The number of synapses located on the dendrites is substantially higher than on the soma. The synaptic input effect on the neuronal excitability decreases with distance between a synapse ending and the trigger zone. Two areas are distinguished in accordance with the effect of synaptic input--dendritic and somatic. The dendritic area, when compared to the soma, is characterized by much higher intensity of its activation but the amplitudes of synaptically evoked changes of the membrane potential at the trigger zone are in general small. This situation is suitable for a diffusion approximation. However, on the soma, especially in the proximity of the trigger zone, the membrane potential changes are a large fraction of the threshold depolarization. The membrane potential at the trigger zone is modelled by a one-dimensional stochastic process. The diffusion Ornstein-Uhlenbeck process serves as a basis of the model; however, at the moments of somatic synapses activation its voltage changes in jumps. Their sizes represent the amplitudes of the evoked postsynaptic potentials. The unimodal histograms of interspike intervals can be explained by the model. The values of the coefficient of variation greater than one are connected with substantial inhibition.

Animals↗