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Rather than model this using networks built from single neuron models, it is has proven especially useful to develop low-dimensional models to mimic the collection of thousands of near identical interconnected neurons with a preference to operate in synchrony.
We model this using stochastic finite state machines, where the parameters of the model are learned from a corpus of human conversations.
We model this using a stochastic process which captures the variability in participants' responses to the same treatment.
Because the scheme is local, there is no a priori assumption that the whole contours comply with a certain mathematical description: we only assume that the contour is smooth between two proximate primitives, and model this using Hermite interpolation.
We model this using a time varying time to event model.
We model this using AC coverages scaled down proportionally to make up for the 11% deficit in coverage of the PC strategy.
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We modeled this using a two-stage approach.
Known as the F81 model, this model uses character state frequency to derive one invariant rate asymmetry for all characters.
To model this, we used a linear approach.
We have modelled this process using our network topology.
In this study, we have tested this model using transgenic zebrafish embryos.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com