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Comparison has been made with a mean field model for the equilibrium brush profile.
The mathematical mean field model, originally proposed by Makeev and Nieuwenhuys (J. Chem. Phys.
The mean field model allows us to discard the presence of stable or unstable periodic orbits for both systems.
There is a good quantitative agreement between the mean field model predictions and numerical results obtained with VAMUCH.
Networks of conductance based excitatory and inhibitory neurons are simulated to characterize different types of transitions to the seizure state, and a mean field model is developed to verify the generality of the observed phenomena of excitatory-inhibitory dynamics.
To explain this fundamental difference between episode onset and termination, we used a mean field model, where only average activity and average level of recovery from synaptic depression are considered.
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The mean-field model provides an accurate prediction of the average stress developed within individual phases.
We then couple the coarse-grained mean-field model to the full stochastic multi-scale model.
We present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions.
Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population.
An incremental mean-field model is developed for the prediction of transformation induced plasticity (TRIP) in multiphase steel.
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