Sentence examples for simulation of networks from inspiring English sources

The code is used to demonstrate the capabilities of the KInNeSS software package for simulation of networks of spiking neurons.

It was designed for the hydraulic and thermal simulation of networks with multiple loop topologies.

Here, we introduce a coarse-grained model that enables simulation of networks of actin filaments, myosin motors, and cross-linking proteins at biologically relevant time and length scales.

Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Goodman, P.H., Harris, F.C.J., Zirpe, M., Natschläger, T., Pecevski, D., Ermentrout, B., Djurfeldt, M., Lansner, A., Rochel, O., Vieville, T., Muller, E., Davison, A., El Boustani, S., and Destexhe, A. Simulation of networks of spiking neurons: a review of tools and strategies.

In this work, great efforts are devoted to taking into account not only local cortical microcircuits but also the global brain architecture, and efficient techniques are proposed and adopted to address the associated computational challenges in simulation of networks of such complexity.

Boolean network modeling was first applied to the study of GRNs in the 1960s [ 34] and has since been used extensively for the simulation of networks in molecular biology [ 35- 41].

The stochastic scheduling algorithm was studied, including simulation of network coverage intensity, delay detection and probability of detection.

For the reason of reproducible mobility situations in cellular real-network simulations at system level, the modeling and simulation of network traffic is an important aspect.

In order to analyze the stability of regulatory core genes alone, we carried out a simulation of network dynamics to determine the stable states of this sub-network in isolation using a Boolean dynamical model.

Moreover, logical hypergraph analyses and Petri net models also have the potential to be used for semi-quantitative simulation of network behavior, since they allow simple predictions of the state of a system in response to different stimuli.

The main goal is to provide a series of benchmark simulations of networks of spiking neurons, and demonstrate how these are implemented in the different simulators overviewed in the paper.