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Here we use chromosome engineering to generate mouse models with gain and loss of a region corresponding to human 1p36.
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Hence, any microscopic model with gain rates f ¯ k, n of such a form reduces to the same Wilson Cowan equation in the limit.
In Fig. 5c, the experimental data and the fitted curves using the model with gain control are shown.
Figure 5d depicts the neuronal distributions in fovea and parafovea estimated by fitting the data to the model with gain control.
This is because the model with gain control more precisely locates the peaks and dips of the plot, although both models can reproduce the qualitative structures (fovea-parafovea difference and N-shaped threshold curve in the parafovea) of the subject's sensitivity.
In contrast, model neurons with gain control show longer-tailed distributions (brown and red curves), indicating that the stimulus information is shared by neurons with a wide variety of preferred spatial frequencies.
However, there is scarcity of literature which connects the normative debate about justifications for different consent models with findings gained in socio-empirical research.
However, there is scarcity of literature which connects the normative debate about justifications for different consent models with findings gained in empirical research.
We found that, for the present problem settings, the model with response gain control provides better fitting performance than the one without.
Quantitative differences between types of rivalry can be explained in this model with different gain factors resulting from various pre-rivalry processing stages [9].
Both criteria selected a structure-based (EX_EHO) model with a gain of BIC per position ranging from 0.48 (segment A) to 0.53 (segment B).
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