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Our first step is to derive analytical expressions for the phase diagram (the regions in parameter space where the system exhibits bistable switching) using a mean-field approximation, in which the consequences of correlations among fluctuations are ignored.
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In this limit, we were not only able to derive comparatively simple expressions for the cross-correlation between two spike trains but were also able to explore analytically the consequences of these correlations for the transmission of time-dependent signals.
The two methods make dissimilar assumptions, but the broad conclusions concerning the consequences of spatial correlation in the landscape structure are the same.
To ensure that these results were not the consequence of spurious correlations, we trained GP models with randomized bioactivity values (y-scrambling) [60].
To ensure that the model's predictive ability was not the consequence of spurious correlations in the data, we trained ten GBM models with an increasingly higher percentage of the pIC50 values randomized.
Interestingly, the consequence of gene correlation over the interaction effect, which is the main contribution of the proposed methodology, was the opposite and makes the method more conservative (see File S6).
We also evaluated the consequences of using more rigorous cut off points (correlation coefficient between two determinants >0.4) or less rigorous cut off points (coefficient >0.8).
Some of the pleiotropy could be a consequence of correlations among traits such as female flower number, fruit number and seed yield, or between diameter and height, which were highly correlated at the phenotypic level.
This would suggest that either the negative correlation observed is a consequence of correlations in activity due to extended feedback projections or that individual units receive relatively weak and diffuse input from many SN neurons rather than strong feedback inhibition from proximally located neurons.
The switch in sign when estimated jointly may be the consequence of the very high correlation existing between the latter variable and the Common_direct_C variable rather than an economic mechanism strictly speaking.
The key point is that genetic relatedness and genetic diversity are inversely correlated and the evolutionary consequences of this correlation are what future modeling endeavors should aim to better understand.
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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