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The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability.
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The model also fits qualitatively better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how images are transformed into scene percepts in the primate ventral stream.
The model fits many parameters.
The model fits reasonably well.
However, they are often chosen by the modeler so as to fit qualitatively the expected phenotypes.
Not all of the model parameters have been measured experimentally, so parameter estimation is not easy, but we can circumvent this issue by simulating a non-dimensionalised version of our model, seeking non-dimensional parameters that produce numerical results that fit qualitatively with experimental observations.
These results fit qualitatively with the experimental observations in the EAE dataset.
This model fitted well the experimental data.
Model fitting.
We recommend joint model fitting.
The model fits are displayed in Figs.
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