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A simple model inspired by the HVC-RA-LMAN network formalized the above intuition and showed that strengthening and pruning of action-specific connections in a motor control network, such as the VMP, reduces its sensitivity to external sources of variability (e.g., LMAN).
We formalize the above assumptions by a prior on Λ that imposes sparsity (formally, shrinkage toward zero) and low effective rank (Bhattacharya and Dunson 2011).
We now formalize our above considerations.
One can formalize what above by the following theorem.
To formalize the above discussion, consider a nonlinear switched model (2).
We proceed to formalize the above descriptions as follows.
We now consider different ways in which the intuitions discussed above can be formalized and generalized.
This means that what I have put into words above has been formalized as equations that can be manipulated to get the sorts of answers I have tried to provide above with qualitative arguments.
Brandenburger and Keisler formalize the above argument in order to prove a very strong impossibility result about the existence of so-called assumption-complete structures.
The training procedure described above can be formalized as Algorithm 1.
This condition can be formalized requiring a sensitivity S above the noise level (C V p = σ p /〈 p〉) of p at steady state, as can be calculated using the stochastic version of the model (see Additional file 1).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com