Sentence examples for modelling of networks from inspiring English sources

One set of modelling efforts has dealt with modelling of networks which interpret gradients of chemical stimuli to make decisions: examples include gradient sensing in chemotaxis (eg. see [ 14, 15]) and pheromone sensing in yeast [ 16].

Our findings, verified by the use of two fundamentally different community detection methods, allow for a classification of real networks and pave the way to a realistic modelling of networks' evolution.

To mimic, and perhaps understand, these behaviors, scientists build computer models of networks of brain cells, known as neural networks, using computational principles inspired by real brains.

Therefore, studying the simplest models of networks, which are ensembles with a small number of elements, becomes of particular interest.

These require simulators that support the modeling of networks and components with different levels of abstraction.

We present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions.

Topics include models of individual nerve cells and their dynamics, models of networks of neurons coupled by synapses and gap junctions, origins and functions of population rhythms in neuronal networks, and models of synaptic plasticity.

The majority of social capital research implicitly relies on the flow model of networks.

Network building tools available within Ingenuity were employed for bioinformatic modeling of networks [58], [59].

Ultimately through, models of networks, especially dark networks must consider their evolving nature, fuzzy boundaries and multiplicities of node classes and diverse relationships.

For statistical modeling of networks, exponential families of distributions offer robust and flexible parametric models [ 24].